Name: Global AI in Drug Discovery Market Size and Trends 2035
Creator: Roots Analysis
Published: 2026-06-25T00:00:01+05:30
License: https://www.rootsanalysis.com/privacy.html
Keywords: AI in Drug Discovery Market, AI in Drug Discovery Market Growth, AI in Drug Discovery Market Trends, AI in Drug Discovery Market Size, AI in Drug Discovery Market Share, AI in Drug Discovery Market Analysis, AI in Drug Discovery Market Forecast, AI in Drug Discovery Market Report, Global AI in Drug Discovery Market, AI in Drug Discovery Industry

Market Outlook

The global AI in drug discovery market, valued at USD 6.0 billion in 2025, is projected to reach USD 8.6 billion in 2026 and USD 25.0 billion by 2035, representing a CAGR of 12.6% during the forecast period 2026 to 2035.

Global AI in Drug Discovery Market Growth 2025 to 2035

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Market Report: Key Takeaways

Market Size & Trends

In terms of application, lead optimization holds the largest share (around 50%) of the current market.
With respect to the type of AI technology, machine learning dominates the current market, accounting for 40% revenue share.
In terms of drug type, small molecules hold the largest share within the current AI in drug discovery market.
Based on the deployment mode, cloud-based deployment dominates the current market.
Based on the therapeutic area, oncological disorders capture the largest share of the current market, and the trend is unlikely to change in the future.
Among end users, pharma and biotech companies are likely to sustain their dominance through 2035 in the AI in drug discovery industry.
In terms of geographical regions, North America dominates the global AI in drug discovery market by securing the largest market share in the current year.

Key Market Statistics

Market Size in 2026: $8.6 Billion
Estimated Market Size in 2035: $25.0 Billion
CAGR (2026-2035): 12.6%
North America: Largest market in 2026
Asia-Pacific: Fastest growing region

Market Introduction

The use of AI tools and platforms in drug discovery is witnessing rapid growth, primarily driven by escalating R&D expenditures and the growing need for innovative therapeutic solutions. The increasing prevalence of chronic conditions, such as cancer, neurological disorders, cardiovascular diseases, and infectious diseases, presents substantial clinical and economic burdens. These challenges are putting pressure on healthcare providers worldwide to develop faster, more effective, and cost-efficient treatment options. Moreover, demographic transition towards an aging population further amplifies these concerns, compelling pharmaceutical and biotechnology companies to integrate artificial intelligence in healthcare and leverage AI-driven drug development platforms in their R&D frameworks.

These AI-driven platforms enhance efficiency across target identification, lead generation, and optimization processes, thereby overcoming key limitations of conventional drug discovery methods, including lengthy development cycles and high attrition rates. Moreover, AI platforms are increasingly being used to analyze vast multi-omics datasets and support applications such as virtual screening, de novo molecule design, and predictive toxicology. The increasing focus towards such capabilities is enabling researchers to identify drug candidates with improved efficacy and safety profiles at an early stage of discovery.

As these platforms become more integrated across discovery workflows, the market is progressing from AI-assisted screening toward AI-native drug design, where AI systems play a central role in creating novel molecules, antibodies, proteins, and therapeutic candidates from the earliest stages of discovery. Companies such as Isomorphic Labs, Insilico Medicine, and Generate are increasingly using AI as a core discovery engine rather than an analytical support tool. For instance, Insilico Medicine announced the recent advancement of ISM001-055 (drug candidate developed using generative AI) into Phase II trials for idiopathic pulmonary fibrosis, demonstrating growing industry confidence in these technologies for reducing drug development timelines.

Consequently, AI-driven solutions are becoming the preferred choice for early-stage discovery among biotech firms and major pharmaceutical companies. Moreover, platform providers are integrating advanced features like multimodal large language models (LLMs) for data extraction, cloud-based workflows, and precision analytics for patient stratification. Sustained investments and strategic collaborations underscore robust market momentum, driving the growth of the AI in drug discovery market size over the foreseen future.

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Leading Companies in AI in Drug Discovery Market

Company	YoE	HQ (Region)	Type of AI Technology Used	Application	Type of Molecule Analyzed
BenevolentAI	2013	Europe	ML (Deep Learning) and LLM	Target Identification/Validation, Lead ID/Optimization	Small Molecules and Biologics
Collaborations Pharmaceuticals	2015	North America	ML (Deep Learning, Supervised Learning, Reinforcement Learning)	Target Identification/Validation, Lead ID/Optimization	Small Molecules
Genialis	2015	North America	ML	Target Identification/Validation, Lead ID/Optimization	Small Molecules
Healx	2014	Europe	ML (Deep Learning) and NLP	Target Identification/Validation, Lead ID/Optimization	Small Molecules
Insilico Medicine	2014	North America	ML (Deep Learning, Reinforcement Learning) and Generative AI	Target Identification/Validation, Lead ID/Optimization	Small Molecules
Optibrium	2009	Europe	ML (Deep Learning)	Lead Identification / Optimization	Small Molecules
XtalPi	2015	Asia-Pacific	ML (Deep Learning), and LLM	Lead Identification / Optimization	Small Molecules and Biologics

Abbreviations: YoE: Year of Establishment; HQ: Headquarters; ML: Machine Learning; LLM: Large Language Model, NLP: Natural Language Processing

Market Segmentation

Based on market research, we have segmented the AI in Drug Discovery Market into application, type of AI technology, drug type, deployment mode, therapeutic area, end user, and geographical regions.

By Application

Target Identification / Validation
Hit Generation / Lead Identification
Lead Optimization

By Type of AI Technology

Machine Learning
Molecular Modelling and Simulation
Deep Learning
Omics Integration
Generative Model
Structure-based Drug Design
Others

By Drug Type

Small Molecules
Biologics

By Deployment Mode

Cloud-based
On-premises
SaaS-based

By Therapeutic Area

Oncological Disorders
Cardiovascular Diseases
Musculoskeletal Diseases
Neurological Disorder
Respiratory Disorders
Immunological Disorders
Gastrointestinal Disorders
Endocrine Disorders
Blood Disorders
Ophthalmological Disorders
Dermatological Disorders
Infectious Diseases
Urinary Disorders

By End User

Pharma and Biotech Companies
Contract Research Organizations
Research and Academic Institutions

By Geographical Regions

North America
US
Canada
Europe
UK
Germany
France
Spain
Italy
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Australia
India
Middle East and North Africa
Saudi Arabia
UAE
Egypt
Latin America
Brazil
Mexico
Argentina

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AI in Drug Discovery Market Key Insights

What Factors are Driving Market Expansion?

Escalating R&D Investments: AI in pharmaceuticals discovery has attracted substantial venture capital investments in recent years, particularly for platforms demonstrating enhanced target identification, molecular interaction prediction, and lead compound optimization. This robust investment trajectory is likely to act as a pivotal driver for sustained market expansion throughout the forecast period.
Expanding Biomedical Datasets: The growing volume of biomedical data from genomics, proteomics, and real-world evidence creates substantial opportunities for machine learning drug discovery. AI platforms excel in pattern recognition across these datasets, uncovering novel targets and enabling drug repurposing, thereby accelerating adoption among biotech firms seeking data-driven efficiency.

What are the Main Challenges for AI in Drug Discovery Market Growth?

Data Integration Complexities: Several companies in the pharmaceutical industry are facing data integration challenges from diverse sources like genomics databases, proteomics repositories, and preclinical assays stored in different formats. This fragmentation complicates AI-driven analysis across discovery pipelines, hindering multi-omics processing and actionable insights for target identification and lead optimization. Standardization initiatives and unified data frameworks are essential to fully harness AI platforms for drug discovery.
Lack of Standardized Regulatory Frameworks: The lack of consistent regulatory guidelines is restricting AI adoption across drug discovery pipelines. The concerns associated with data security, privacy, and intellectual property protection for proprietary biomedical and chemical data demand clearer frameworks. As a result, companies restrict access to critical datasets, such as compound libraries and clinical testing outcomes, thereby limiting collaborative model refinement and reducing insights for lead generation.

What are the Technological Advancements in AI in Drug Discovery Domain?

As per our AI in drug discovery market analysis, platform providers are increasingly deploying generative AI and deep learning-based platforms with reinforcement learning to design novel molecules. These tools evaluate billions of candidates within days against complex CNS and immunology targets, signaling a transition from conventional trial-and-error approaches to data-driven drug development.

Notable examples of platforms validating the ongoing technological shift in this industry include PandaOmics (Insilico Medicine), ASCEND (BenchSci) and Recursion OS, which offer integrated multimodal LLMs to extract insights from patents and clinical trials alongside hybrid quantum models for enhanced protein simulations. These advancements accelerate target validation and predictive modeling, enabling more precise molecule optimization and reduced discovery timelines through seamless integration of diverse data modalities.

Big Pharma is Moving from Pilots to Billion-Dollar Partnerships

Pharmaceutical companies are increasingly moving beyond small-scale AI pilots toward large strategic collaborations that embed AI platforms into core drug discovery programs. For instance, in February 2026, Takeda entered into a multi-year partnership worth over USD 1.7 billion with Iambic to use AI for designing small-molecule drugs targeting cancer and gastrointestinal diseases. Similarly, in March 2026, Eli Lilly expanded its collaboration with Insilico Medicine in a deal worth up to USD 2.75 billion to leverage Insilico’s AI engine for preclinical oral therapies. These high-value agreements reflect growing confidence among large pharmaceutical companies in AI-enabled discovery platforms and are likely to accelerate the adoption of AI-native drug development models across the industry.

Recent Developments in AI in Drug Discovery Market

In June 2026, Nxera Pharma joined OpenFold, an AI research consortium focused on open-source tools for biology and drug discovery. Through the consortium, Nxera aims to strengthen its AI-enabled GPCR drug discovery capabilities, benchmark structural prediction models and improve progression from target validation to clinical development.
In June 2026, Owkin entered into a multi-year collaboration with Sanofi to co-develop AI-driven biopharma agents through K Pro, Owkin’s AI Scientist platform. The collaboration builds on the companies’ existing oncology and immunology work and aims to help Sanofi accelerate decision-making across drug discovery and development workflows.
In June 2026, DaltonTx launched Dalton, an agentic AI-enabled drug discovery platform, and entered into a collaboration with Sygnature Discovery. As part of the collaboration, Sygnature will evaluate the platform across a legacy oncology program to assess whether AI-supported decision-making can reduce synthesis burden and improve candidate selection efficiency.
In May 2026, Quotient Sciences initiated a Phase I clinical study of an AI-designed oral formulation. The study aims to validate the use of AI in formulation design and highlights the growing role of machine learning in accelerating drug product development and improving early clinical decision-making.

Industry Experts on AI in Drug Discovery Market

The market for AI in drug discovery has a transformative future and is likely to remain progressive in the future. Reflecting this shift, James Halle (Chief Commercial Officer of Optibrium), stated that, “Nearly every biotech and biopharma company is exploring or deploying AI in some capacity. However, very few can quantify the value they are generating from these initiatives. At Optibrium, we prioritize proving measurable impact; for example, recent projects using Cerella demonstrated a 70–80% reduction in synthetic and experimental effort. Alternatively, companies could evaluate two to four times more compounds with the same budget”

Discussions with multiple stakeholders in this domain influenced the opinions and insights presented in this study. The market report includes transcripts of the following discussions:

Co-founder, Chairman and Chief Executive Officer, Small Company, US
Chief Executive Officer and Co-Founder, Small Company, Israel
Chief Commercial Officer and Chief Product Officer, Small Company, UK
Chief Executive Officer, Small Company, UK
Chief Commercial Officer, Mid-sized Company, UK
Chairman, Small Company, US
Head Researcher, Mid-sized Company, South Korea

In addition, the market report includes transcripts of the following other third-party discussions:

Founder, Chief Executive Officer, Small Company, India
Chief Executive Officer, Mid-sized Company, US
Chief Executive Officer, Small Company, US
Chief Executive Officer and Chief AI and Innovation Officer, Small Company, US
Chief R&D Officer and Chief Commercial Officer, Large Company, US
Chief Corporate Development and Strategy Officer, Mid-sized Company, UK
Co-founder and Chair of the Scientific Advisory Board, Small Company, US
Former Non-Executive Director, Mid-sized Company, UK
Principal Consultant, Data and AI, Large Company, US
Technical and Sales Manager, Mid-sized Company, UK

Which Application accounts for the Largest Share?

Based on application, the global AI in drug discovery market is segmented into target identification / validation, hit generation / lead identification, and lead optimization. Across related applications, fragment based drug discovery screens low-molecular-weight fragments to establish efficient starting points for lead optimization.

Currently, lead optimization accounts for around 50% of the overall AI in drug discovery market size. This dominance is primarily driven by the highly iterative nature of the lead optimization process, where researchers must evaluate and refine large numbers of candidate molecules before selecting a clinical development candidate. AI-driven platforms enable predictive modeling, virtual screening, and rapid evaluation of molecular properties, allowing researchers to prioritize the most promising candidates while reducing experimental burden. As pharmaceutical companies continue to focus on reducing development timelines and lowering experimental costs, adoption of AI-enabled lead optimization platforms is expected to accelerate.

Global AI in Drug Discovery Market Share by Application, 2026

It is worth highlighting that the market for target identification / validation is expected to witness significant growth during the forecast period. Advances in data analytics and the growing availability of biological and genetic datasets are enabling AI platforms to identify novel disease targets more efficiently than traditional approaches. As pharmaceutical companies increasingly focus on identifying high-potential therapeutic targets earlier in the drug discovery process, the demand for AI-driven target identification / validation platforms is expected to increase substantially.

Which AI Technology captures the Largest Share of AI in Drug Discovery Market?

Based on the type of AI technology, the global AI in drug discovery market is segmented into machine learning, molecular modeling and simulation, deep learning, omics integration, generative models, structure-based drug design, and others.

Machine learning currently accounts for 40% of the current market share. Its leadership can be attributed to its broad applicability across multiple stages of the drug discovery process, ranging from target identification and compound screening to lead optimization and candidate selection. Unlike several emerging AI technologies that are often deployed for specific use cases, machine learning platforms can analyze large and diverse datasets to identify patterns, predict molecular properties, and support decision-making throughout the drug development lifecycle. Further, the availability of established algorithms, growing computational capabilities, and increasing access to biological and chemical datasets have accelerated the adoption of machine learning across the pharmaceutical industry.

The deep learning segment is expected to witness the highest growth during the forecast period. This growth is driven by industry’s increasing focus on complex drug discovery programs involving novel targets, biologics, and precision medicine approaches. Unlike traditional analytical models, deep learning models can analyze intricate biological relationships and support the development of sophisticated predictive and generative drug discovery tools. Further, advancements in computing infrastructure and AI model architecture are enabling broader adoption of deep learning across applications such as protein structure prediction, molecular design, and biological pathway analysis, creating significant opportunities for future market expansion.

Global AI in Drug Discovery Market Share by Type of AI Technology, 2026

Amongst Small Molecules and Biologics, Which Segment holds the Largest Share in the AI in Drug Discovery Market?

Based on the drug type, the AI in drug discovery market is segmented into small molecules and biologics.

Small molecules currently hold the majority share of the market. This dominance is due to the long-established role of small molecules within the pharmaceutical industry and the vast volume of historical chemical, biological, and clinical data available for AI model development. Unlike biologics, small molecules typically involve well-defined molecular structures and established drug discovery workflows, making them the ideal choice for computational modeling and virtual screening approaches.

Further, pharmaceutical companies continue to prioritize small molecule programs due to their scalability, manufacturing feasibility, and broad applicability across multiple therapeutic areas. As a result, AI technologies have been widely integrated into small molecule discovery workflows to improve candidate selection, optimize molecular properties, and accelerate early-stage drug development.

Biologics are expected to witness faster growth during the forecast period. This growth is driven by increasing industry interest in complex therapeutic modalities, including monoclonal antibodies, engineered proteins, and other advanced biologic products. As researchers seek to address disease targets that are often difficult to modulate using conventional small molecules, AI platforms are increasingly being utilized to support biologics design, protein engineering, and candidate optimization.

Which Deployment Mode Holds the Largest Share within the AI in Drug Discovery Market?

Based on deployment mode, the AI in drug discovery market is segmented into cloud-based, on-premises, and SaaS-based deployment.

Currently, cloud-based deployment accounts for the largest share of the market. This dominance is primarily driven by the significant computational resources required to process large-scale biological, chemical, and clinical datasets used in modern drug discovery. Cloud-based platforms enable organizations to access scalable computing infrastructure without substantial upfront investments in hardware and IT resources. Further, these platforms facilitate data sharing, collaborative research, and seamless integration of AI tools across geographically dispersed teams, making them particularly suitable for pharmaceutical and biotechnology companies seeking flexibility and operational efficiency.

It is worth highlighting that SaaS-based deployment is expected to witness the highest growth during the forecast period. This growth is driven by the increasing demand for AI solutions that can be implemented without substantial upfront investments in computational infrastructure, software licensing, or dedicated IT resources. By offering subscription-based access to advanced AI capabilities, SaaS platforms enable organizations of varying sizes to adopt AI-driven drug discovery tools while reducing implementation complexity. Further, these platforms simplify software maintenance, upgrades, and scalability, allowing users to access the latest AI technologies without managing extensive in-house infrastructure.

Which Therapeutic Area Holds the Largest Share of the AI in Drug Discovery Market?

Based on therapeutic area, the AI in drug discovery market is segmented into oncological disorders, cardiovascular disorders, musculoskeletal disorders, neurological disorders, respiratory disorders, immunological disorders, gastrointestinal disorders, endocrine disorders, blood disorders, ophthalmological disorders, dermatological disorders, infectious diseases, and urinary disorders.

Currently, oncological disorders account for the largest share of the market, and this trend is expected to continue throughout the forecast period. This dominance can be attributed to the complexity and heterogeneity of cancer biology, which requires the analysis of vast volumes of genomic, proteomic, clinical, and molecular data during the drug discovery process. AI technologies have emerged as valuable tools for identifying novel drug targets, predicting biomarker responses, optimizing candidate selection, and supporting precision oncology initiatives. Further, oncological disorders continue to attract a significant proportion of global pharmaceutical research investment, resulting in a large and diverse pipeline of drug development programs where AI can be applied to improve discovery efficiency and decision-making.

It is worth highlighting that the oncological disorders segment is also expected to witness the highest growth during the forecast period. This growth is driven by the increasing adoption of data-driven drug discovery approaches aimed at addressing highly complex cancer pathways and improving the probability of clinical success.

What is the Market Opportunity across Different End Users in the AI in Drug Discovery Market?

Based on end user, the AI in drug discovery market is segmented into pharma and biotech companies, contract research organizations, and research and academic institutions.

Currently, pharma and biotech companies account for the largest share of the market. This dominance can be attributed to their large-scale drug discovery and development programs, where reducing development timelines, improving candidate quality, and optimizing research productivity remain strategic priorities. Further, these organizations possess the financial resources, proprietary datasets, and technical infrastructure required to deploy AI technologies across multiple stages of drug discovery workflow. As competition among leading pharma and biotech companies increases across therapeutic areas, firms are expected to increase investments in AI-driven platforms to enhance decision-making and improve the efficiency of their drug discovery programs.

It is worth highlighting that the contract research organizations (CRO) segment is expected to witness the highest growth during the forecast period. This growth is driven by the increasing outsourcing of drug discovery activities by pharmaceutical and biotechnology companies seeking to access specialized expertise while improving operational flexibility. As clients increasingly demand faster and more cost-efficient discovery services, CROs are integrating AI technologies into their service offerings to support target identification, hit discovery, lead optimization, and data analysis.

Regional Analysis: Which Regions are Showing the Fastest AI in Drug Discovery Market Growth?

North America: Dominating the Market by Securing Highest Share

North America accounts for more than 50% of the current market share of the AI in drug discovery industry. Several factors, such as substantial R&D investments, advanced healthcare IT infrastructure, and favorable FDA regulatory frameworks supporting the application of AI / ML technologies are fueling the market growth in this region. Further, North America has been at the forefront of integrating artificial intelligence into drug discovery workflows, supported by substantial investments in computational infrastructure, advanced data analytics capabilities, and biomedical research. The presence of several leading AI-driven drug discovery companies and strategic collaborations between technology providers and pharmaceutical developers has further strengthened the region's position within the global market. Across related applications, DNA encoded library enables high-throughput affinity screening of chemically diverse, barcoded small-molecule libraries.

Asia-Pacific: An Emerging Growth Spot of AI in Drug Discovery Market

This growth is propelled by accelerated AI infrastructure development, bolstered by national strategies including China's "Made in China 2025" initiative and India's National Strategy for Artificial Intelligence. Moreover, substantial public and private sector investments in big data in healthcare, and availability of extensive and heterogeneous patient datasets, are fueling the adoption of AI drug discovery platforms in the region. As regional stakeholders expand research and development efforts, Asia-Pacific is expected to play an increasingly important role in the AI in drug discovery market.

What are the Mega Trends in AI in Drug Discovery Industry?

Expanding Applications of Generative AI: Generative AI models, such as GANs (Generative Adversarial Networks), transformer-based architecture, and reinforcement learning, enable the creation of novel chemical structures optimized for binding affinity, ADMET properties, and synthesizability. This shifts drug discovery from screening existing libraries to exploring inaccessible chemical spaces, compressing hit-to-lead timelines from years to months, particularly for cancer treatment and neurodegeneration targets. For instance, Merck KGaA has developed AIDDISON™, a generative AI-powered platform that integrates deep learning with computer-aided drug design to accelerate the identification and optimization of novel drug candidates.

Emergence of Autonomous AI Labs: Self-operating laboratories, powered by AI-driven robotics, execute continuous design-build-test-learn (DBTL) cycles, autonomously conducting thousands of experiments continuously. These systems integrate with generative AI workflows to accelerate hit identification and optimization. They further support preclinical development by shifting from traditional human-led approaches to hybrid human-AI models that enhance laboratory productivity and scalability in drug discovery.

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Scope of AI in Drug Discovery Market Report

Key Report Attribute	Details
Historical Trend	Since 2023
Forecast Period	Till 2035
Market Size 2026	USD 8.6 Billion
Market Size 2035	USD 25.0 Billion
CAGR (Till 2035)	12.6%
Segments Covered	Application Type of AI Technology Drug Type Deployment Mode Therapeutic Area End User Geographical Regions
Key Players Profiled	BenevolentAI Collaborations Pharmaceuticals CytoReason Deargen Deep Genomics Genialis Healx Insilico Medicine Iktos Optibrium XtalPi (A complete list of companies captured is available in the report)
PowerPoint Presentation Complimentary)	Available
Customization Scope	15% Free Customization
Excel Data Packs (Complimentary)	Available

+ 1. PREFACE

1.1. Introduction

1.2. Market Share Insights

1.3. Key Market Insights

1.4. Report Coverage

1.5. Key Questions Answered

+ 2. RESEARCH METHODOLOGY

2.1. Chapter Overview

2.2. Research Assumptions

2.2.1. Market Landscape and Market Trends

2.2.2. Market Forecast and Opportunity Analysis

2.2.3. Comparative Analysis

2.3. Database Building

2.3.1. Data Collection

2.3.2. Data Validation

2.3.3. Data Analysis

2.4. Project Methodology

2.4.1. Secondary Research

2.4.1.1. Annual Reports

2.4.1.2. Academic Research Papers

2.4.1.3. Company Websites

2.4.1.4. Investor Presentations

2.4.1.5. Regulatory Filings

2.4.1.6. White Papers

2.4.1.7. Industry Publications

2.4.1.8. Conferences and Seminars

2.4.1.9. Government Portals

2.4.1.10. Media and Press Releases

2.4.1.11. Newsletters

2.4.1.12. Industry Databases

2.4.1.13. Roots Proprietary Databases

2.4.1.14. Paid Databases and Sources

2.4.1.15. Social Media Portals

2.4.1.16. Other Secondary Sources

2.4.2. Primary Research

2.4.2.1. Types of Primary Research

2.4.2.1.1. Qualitative Research

2.4.2.1.2. Quantitative Research

2.4.2.1.3. Hybrid Approach

2.4.2.2. Advantages of Primary Research

2.4.2.3. Techniques for Primary Research

2.4.2.3.1. Interviews

2.4.2.3.2. Surveys

2.4.2.3.3. Focus Groups

2.4.2.3.4. Observational Research

2.4.2.3.5. Social Media Interactions

2.4.2.4. Key Opinion Leaders Considered in Primary Research

2.4.2.4.1. Company Executives (CXOs)

2.4.2.4.2. Board of Directors

2.4.2.4.3. Company Presidents and Vice Presidents

2.4.2.4.4. Research and Development Heads

2.4.2.4.5. Technical Experts

2.4.2.4.6. Subject Matter Experts

2.4.2.4.7. Scientists

2.4.2.4.8. Doctors and Other Healthcare Providers

2.4.2.5. Ethics and Integrity

2.4.2.5.1. Research Ethics

2.4.2.5.2. Data Integrity

2.4.3. Analytical Tools and Databases

2.5. Robust Quality Control

+ 3. MARKET DYNAMICS

3.1. Chapter Overview

3.2. Forecast Methodology

3.2.1. Top-down Approach

3.2.2. Bottom-up Approach

3.2.3. Hybrid Approach

3.3. Market Assessment Framework

3.3.1. Total Addressable Market (TAM)

3.3.2. Serviceable Addressable Market (SAM)

3.3.3. Serviceable Obtainable Market (SOM)

3.3.4. Currently Acquired Market (CAM)

3.4. Forecasting Tools and Techniques

3.4.1. Qualitative Forecasting

3.4.2. Correlation

3.4.3. Regression

3.4.4. Extrapolation

3.4.5. Convergence

3.4.6. Sensitivity Analysis

3.4.7. Scenario Planning

3.4.8. Data Visualization

3.4.9. Time Series Analysis

3.4.10. Forecast Error Analysis

3.5. Key Considerations

3.5.1. Demographics

3.5.2. Government Regulations

3.5.3. Reimbursement Scenarios

3.5.4. Market Access

3.5.5. Supply Chain

3.5.6. Industry Consolidation

3.5.7. Pandemic / Unforeseen Disruptions Impact

3.6. Limitations

+ 4. MACRO-ECONOMIC INDICATORS

4.1. Chapter Overview

4.2. Market Dynamics

4.2.1. Time Period

4.2.1.1. Historical Trends

4.2.1.2. Current and Forecasted Estimates

4.2.2. Currency Coverage

4.2.2.1. Major Currencies Affecting the Market

4.2.2.2. Factors Affecting Currency Fluctuations on the Industry

4.2.2.3. Impact of Currency Fluctuations on the Industry

4.2.3. Foreign Currency Exchange Rate

4.2.3.1. Impact of Foreign Exchange Rate Volatility on the Market

4.2.3.2. Strategies for Mitigating Foreign Exchange Risk

4.2.4. Recession

4.2.4.1. Assessment of Current Economic Conditions and Potential Impact on the Market

4.2.4.2. Historical Analysis of Past Recessions and Lessons Learnt

4.2.5. Inflation

4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy

4.2.5.2. Potential Impact of Inflation on the Market Evolution

4.2.6. Interest Rates

4.2.6.1. Interest Rates and Their Impact on the Market

4.2.6.2. Strategies for Managing Interest Rate Risk

4.2.7. Commodity Flow Analysis

4.2.7.1. Type of Commodity

4.2.7.2. Origins and Destinations

4.2.7.3. Values and Weights

4.2.7.4. Modes of Transportation

4.2.8. Global Trade Dynamics

4.2.8.1. Import Scenario

4.2.8.2. Export Scenario

4.2.8.3. Trade Policies

4.2.8.4. Strategies for Mitigating the Risks Associated with Trade Barriers

4.2.8.5. Impact of Trade Barriers on the Market

4.2.9. War Impact Analysis

4.2.9.1. Russian-Ukraine War

4.2.9.2. Israel-Hamas War

4.2.10. COVID Impact / Related Factors

4.2.10.1. Global Economic Impact

4.2.10.2. Industry-specific Impact

4.2.10.3. Government Response and Stimulus Measures

4.2.10.4. Future Outlook and Adaptation Strategies

4.2.11. Other Indicators

4.2.11.1. Fiscal Policy

4.2.11.2. Consumer Spending

4.2.11.3. Gross Domestic Product

4.2.11.4. Employment

4.2.11.5. Taxes

4.2.11.6. Stock Market Performance

4.2.11.7. Cross Border Dynamics

4.3. Conclusion

+ 5. EXECUTIVE SUMMARY

5.1. Chapter Overview

5.2. AI-based Drug Discovery: Market Landscape

5.3. AI-based Drug Discovery: Market Trends

5.4. AI-based Drug Discovery: Market Forecast and Opportunity Analysis

+ 6. INTRODUCTION

6.1. Chapter Overview

6.2. Artificial Intelligence

6.3. Subsets of AI

6.3.1. Machine Learning

6.3.1.1. Supervised Learning

6.3.1.2. Unsupervised Learning

6.3.1.3. Reinforced / Reinforcement Learning

6.3.1.4. Deep Learning

6.3.1.5. Large Language Models (LLMs)

6.3.1.6. Natural Language Processing

6.3.1.7. Generative AI

6.3.1.8. Computer Vision

6.4. Applications of AI in Healthcare

6.4.1. Drug Discovery

6.4.2. Disease Prediction, Diagnosis and Treatment

6.4.3. Manufacturing and Supply Chain Operations

6.4.4. Drug Marketing

6.4.5. Clinical Trials

6.5. AI in Drug Discovery

6.5.1. Target Identification

6.5.2. Identification of Hit or Lead

6.5.3. Lead Optimization

6.6. Advantages of Using AI in Drug Discovery Process

6.7. Challenges Associated with the Adoption of AI

6.8. Future Perspective

+ 7. MARKET LANDSCAPE

7.1. Chapter Overview

7.2. AI-based Drug Discovery Platform Providers: Overall Market Landscape

7.2.1. Analysis by Year of Establishment

7.2.2. Analysis by Company Size

7.2.3. Analysis by Location of Headquarters

7.3. Key AI-based Drug Discovery Platform Providers: Market Landscape

7.3.1. Analysis by Type of Business Model

7.3.2. Analysis by Platform Utilization

7.3.3. Analysis by Drug Discovery Stages Supported

7.3.4. Analysis by Type of AI Technology Used

7.3.5. Analysis by Target Therapeutic Area

7.3.6. Analysis by Type of Molecule Analyzed

7.3.7. Analysis by End User

+ 8. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN NORTH AMERICA

8.1. Chapter Overview

8.2. Collaborations Pharmaceuticals

8.2.1. Company Overview

8.2.2. AI-based Drug Discovery Platform / Technology Portfolio

8.2.3. Recent Developments and Future Outlook

8.3. Deep Genomics

8.3.1. Company Overview

8.3.2. AI-based Drug Discovery Platform / Technology Portfolio

8.3.3. Recent Developments and Future Outlook

8.4. Genialis

8.4.1. Company Overview

8.4.2. AI-based Drug Discovery Platform / Technology Portfolio

8.4.3. Recent Developments and Future Outlook

8.5. Insilico Medicine

8.5.1. Company Overview

8.5.2. AI-based Drug Discovery Platform / Technology Portfolio

8.5.3. Recent Developments and Future Outlook

8.6. XtalPi

8.6.1. Company Overview

8.6.2. AI-based Drug Discovery Platform / Technology Portfolio

8.6.3. Recent Developments and Future Outlook

+ 9. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN EUROPE

9.1. Chapter Overview

9.2. BenevolentAI

9.2.1. Company Overview

9.2.2. AI-based Drug Discovery Platform / Technology Portfolio

9.2.3. Recent Developments and Future Outlook

9.3. Healx

9.3.1. Company Overview

9.3.2. AI-based Drug Discovery Platform / Technology Portfolio

9.3.3. Recent Developments and Future Outlook

9.4. Iktos

9.4.1. Company Overview

9.4.2. AI-based Drug Discovery Platform / Technology Portfolio

9.4.3. Recent Developments and Future Outlook

9.5. Optibrium

9.5.1. Company Overview

9.5.2. AI-based Drug Discovery Platform / Technology Portfolio

9.5.3. Recent Developments and Future Outlook

+ 10. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN ASIA-PACIFIC AND REST OF WORLD

10.1. Chapter Overview

10.2. CytoReason

10.2.1. Company Overview

10.2.2. AI-based Drug Discovery Platform / Technology Portfolio

10.2.3. Recent Developments and Future Outlook

10.3. Deargen

10.3.1. Company Overview

10.3.2. AI-based Drug Discovery Platform / Technology Portfolio

10.3.3. Recent Developments and Future Outlook

+ 11. PARTNERSHIPS AND COLLABORATIONS

11.1. Chapter Overview

11.2. Partnership Models

11.3. AI-based Drug Discovery: Partnerships and Collaborations

11.3.1. Quarterly Trend of Partnerships

11.3.2. Analysis by Type of Partnership

11.3.3. Analysis by Quarter and Type of Partnership

11.3.4. Analysis by Type of Partner

11.3.5. Most Active Players: Analysis by Number of Partnerships

11.3.6. Analysis by Geography

11.3.6.1. Intercontinental and Intracontinental Deals

11.3.6.2. International and Local Deals

+ 12. FUNDING AND INVESTMENTS ANALYSIS

12.1. Chapter Overview

12.2. Funding Models

12.3. AI-based Drug Discovery: Funding and Investments

12.3.1. Quarterly Trend of Funding

12.3.2. Quarterly Trend of Amount Invested

12.3.3. Analysis of Funding Instances by Type of Funding

12.3.4. Analysis of Funding Instances by Quarter and Type of Funding

12.3.5. Analysis of Amount Invested by Type of Funding

12.3.6. Analysis of Amount Invested by Quarter and Type of Funding

12.3.7. Analysis by Geography

12.3.8. Most Active Players: Analysis by Number of Funding Instances

12.3.9. Most Active Players: Analysis by Amount Raised

12.3.10. Leading Investors: Distribution by Number of Funding Instances

+ 13. PATENT ANALYSIS

13.1. Chapter Overview

13.2. Scope And Methodology

13.3. AI-based Drug Discovery: Patent Analysis

13.3.1. Analysis by Patent Publication Year

13.3.2. Analysis by Type of Patent and Publication Year

13.3.3. Analysis by Patent Application Year

13.3.4. Analysis by Patent Jurisdiction

13.3.5. Analysis by CPC Symbols

13.3.6. Analysis by Type of Applicant

13.3.7. Leading Industry Players: Analysis by Number of Patents

13.3.8. Leading Non-Industry Players: Analysis by Number of Patents

13.3.9. Leading Individual Assignees: Analysis by Number of Patents

13.4. Patent Benchmarking Analysis

13.4.1. Analysis by Patent Characteristics

13.5. Patent Valuation

13.6. Leading Patents by Number of Citations

+ 14. PORTER'S FIVE FORCES ANALYSIS

14.1. Chapter Overview

14.2. Methodology and Assumptions

14.3. Key Elements of Porter's Five Forces

14.4. Threat of New Entrants

14.5. Bargaining Power of Buyers

14.6. Bargaining Power of Solution Providers

14.7. Threats of Substitute Products

14.8. Rivalry Among Existing Competitors

14.9. Concluding Remarks

+ 15. COMPANY VALUATION ANALYSIS

15.1. Chapter Overview

15.2. Company Valuation Analysis: Key Parameters

15.3. Methodology

15.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

+ 16. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

16.1. Chapter Overview

16.2. Alibaba Cloud

16.3. Google

16.4. IBM

16.5. Intel

16.6. Microsoft

16.7. Siemens

+ 17. COST SAVING ANALYSIS

17.1. Chapter Overview

17.2. Key Assumptions and Methodology

17.3. Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035

17.3.1. Cost Saving Potential: Distribution by Application

17.3.1.1. Cost Saving Potential in Target Identification / Validation, Till 2035

17.3.1.2. Cost Saving Potential in Hit Generation / Lead Identification and Optimization, Till 2035

17.3.2. Cost Saving Potential: Distribution by Type of AI Technology

17.3.2.1. Cost Saving Potential with Machine Learning, Till 2035

17.3.2.2. Cost Saving Potential with Molecular Modelling and Simulation, Till 2035

17.3.2.3. Cost Saving Potential with Deep Learning, Till 2035

17.3.2.4. Cost Saving Potential with Omics Integration, Till 2035

17.3.2.5. Cost Saving Potential with Generative Model, Till 2035

17.3.2.6. Cost Saving Potential with Structure-based Drug Design, Till 2035

17.3.2.7. Cost Saving Potential with Other Technologies, Till 2035

17.3.3. Cost Saving Potential: Distribution by Therapeutic Area, Till 2035

17.3.3.1. Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035

17.3.3.2. Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035

17.3.3.3. Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035

17.3.3.4. Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035

17.3.3.5. Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035

17.3.3.6. Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035

17.3.3.7. Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035

17.3.3.8. Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035

17.3.3.9. Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035

17.3.3.10. Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035

17.3.3.11. Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035

17.3.3.12. Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035

17.3.3.13. Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035

17.3.4. Cost Saving Potential: Distribution by End User

17.3.4.1. Cost Saving Potential for Pharma and Biotech Companies, Till 2035

17.3.4.2. Cost Saving Potential for Contract Research Organizations (CROs), Till 2035

17.3.4.3. Cost Saving Potential for Research and Academic Institutions, Till 2035

17.3.5. Cost Saving Potential: Distribution by Geographical Regions

17.3.5.1. Cost Saving Potential in North America, Till 2035

17.3.5.2. Cost Saving Potential in Europe, Till 2035

17.3.5.3. Cost Saving Potential in Asia-Pacific, Till 2035

17.3.5.4. Cost Saving Potential in MENA, Till 2035

17.3.5.5. Cost Saving Potential in Latin America, Till 2035

17.4. Conclusion

+ 18. MARKET IMPACT ANALYSIS

18.1. Chapter Overview

18.2. Market Drivers

18.3. Market Restraints

18.4. Market Opportunities

18.5. Market Challenges

18.6. Conclusion

+ 19. GLOBAL AI IN DRUG DISCOVERY MARKET

19.1. Chapter Overview

19.2. Key Assumptions and Methodology

19.3. Global AI in Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

19.3.1. Scenario Analysis

19.3.1.1. Conservative Scenario

19.3.1.2. Optimistic Scenario

19.4. Key Market Segmentations

+ 20. AI IN DRUG DISCOVERY MARKET, BY APPLICATION

20.1. Chapter Overview

20.2. Key Assumptions and Methodology

20.3. AI in Drug Discovery Market: Distribution by Application

20.3.1. AI in Drug Discovery Market for Target Identification / Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

20.3.2. AI in Drug Discovery Market for Hit Generation / Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

20.3.3. AI in Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

20.4. Data Triangulation and Validation

+ 21. AI IN DRUG DISCOVERY MARKET, BY TYPE OF AI TECHNOLOGY

21.1. Chapter Overview

21.2. Key Assumptions and Methodology

21.3.1. AI in Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.2. AI in Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.3. AI in Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.4. AI in Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.5. AI in Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.6. AI in Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.3.7. AI in Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

21.4. Data Triangulation and Validation

+ 22. AI IN DRUG DISCOVERY MARKET, BY DRUG TYPE

22.1. Chapter Overview

22.2. Key Assumptions and Methodology

22.3.1. AI in Drug Discovery Market for Small Molecules, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

22.3.2. AI in Drug Discovery Market for Biologics, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

22.4. Data Triangulation and Validation

+ 23. AI IN DRUG DISCOVERY MARKET, BY DEPLOYMENT MODE

23.1. Chapter Overview

23.2. Key Assumptions and Methodology

23.3.1. AI in Drug Discovery Market for Cloud-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

23.3.2. AI in Drug Discovery Market for On-premises Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

23.3.3. AI in Drug Discovery Market for SaaS-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

23.4. Data Triangulation and Validation

+ 24. AI IN DRUG DISCOVERY MARKET, BY THERAPEUTIC AREA

24.1. Chapter Overview

24.2. Key Assumptions and Methodology

24.3. AI in Drug Discovery Market: Distribution by Therapeutic Area

24.3.1. AI in Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.2. AI in Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.3. AI in Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.4. AI in Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.5. AI in Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.6. AI in Drug Discovery Market for Immunological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.7. AI in Drug Discovery Market for Gastrointestinal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.8. AI in Drug Discovery Market for Endocrine Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.9. AI in Drug Discovery Market for Blood Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.10. AI in Drug Discovery Market for Ophthalmological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.11. AI in Drug Discovery Market for Dermatological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.12. AI in Drug Discovery Market for Infectious Diseases, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.3.13. AI in Drug Discovery Market for Urinary Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

24.4. Data Triangulation and Validation

+ 25. AI IN DRUG DISCOVERY MARKET, BY END USER

25.1. Chapter Overview

25.2. Key Assumptions and Methodology

25.3. AI in Drug Discovery Market: Distribution by End User

25.3.1. AI in Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

25.3.2. AI in Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

25.3.3. AI in Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

25.4. Data Triangulation and Validation

+ 26. AI IN DRUG DISCOVERY MARKET, BY GEOGRAPHICAL REGIONS

26.1. Chapter Overview

26.2. Key Assumptions and Methodology

26.3. AI in Drug Discovery Market: Distribution by Geographical Regions

26.3.1. AI in Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.1.1. AI in Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.1.2. AI in Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2. AI in Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.1. AI in Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.2. AI in Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.3. AI in Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.4. AI in Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.5. AI in Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.2.6. AI in Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3. AI in Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3.1. AI in Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3.2. AI in Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3.3. AI in Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3.4. AI in Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.3.5. AI in Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.4. AI in Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.4.1. AI in Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.4.2. AI in Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.4.3. AI in Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.5. AI in Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.5.1. AI in Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.5.2. AI in Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.3.5.3. AI in Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)

26.4. Market Dynamics Assessment

26.4.1. Market Movement Analysis

26.4.2. Penetration-Growth (P-G) Matrix

26.5. Data Triangulation and Validation

+ 27. CONCLUDING REMARKS

+ 28. EXECUTIVE INSIGHTS

28.1. Chapter Overview

28.2. Company A

28.2.1. Company Snapshot

28.2.2. Interview Transcript: Chief Commercial Officer and Chief Product Officer

28.3. Company B

28.3.1. Company Snapshot

28.3.2. Interview Transcript: Co-founder, Chairman and Chief Executive Officer

28.4. Company C

28.4.1. Company Snapshot

28.4.2. Interview Transcript: Head Researcher

28.5. Company D

28.5.1. Company Snapshot

28.5.2. Interview Transcript: Chief Executive Officer

28.6. Company E

28.6.1. Company Snapshot

28.6.2. Interview Transcript: Chief Commercial Officer

28.7. Company F

28.7.1. Company Snapshot

28.7.2. Interview Transcript: Chief Executive Officer and Co-Founder

28.8. Company G

28.8.1. Company Snapshot

28.8.2. Interview Transcript: Chairman

28.9. Company H

28.9.1. Company Snapshot

28.9.2. Interview Transcript: Founder and Chief Executive Officer

28.10. Company I

28.10.1. Company Snapshot

28.10.2. Interview Transcript: Chief Executive Officer

28.11. Company J

28.11.1. Company Snapshot

28.11.2. Interview Transcript: Chief Executive Officer

28.12. Company K

28.12.1. Company Snapshot

28.12.2. Interview Transcript: Chief Executive Officer and Chief AI and Innovation Officer

28.13. Company L

28.13.1. Company Snapshot

28.13.2. Interview Transcript: Former Non-Executive Director

28.14. Company M

28.14.1. Company Snapshot

28.14.2. Interview Transcript: Chief R&D Officer and Chief Commercial Officer

28.15. Company N

28.15.1. Company Snapshot

28.15.2. Interview Transcript: Chief Corporate Development and Strategy Officer

28.16. Company O

28.16.1. Company Snapshot

28.16.2. Interview Transcript: Co-founder and Chair of the Scientific Advisory Board

28.17. Company P

28.17.1. Company Snapshot

28.17.2. Interview Transcript: Principal Consultant, Data and AI

28.18. Company Q

28.18.1. Company Snapshot

28.18.2. Interview Transcript: Technical and Sales Manager

+ 29. APPENDIX I: TABULATED DATA

+ 30. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

+ List of Figures

Figure 2.1 Research Methodology: Project Methodology

Figure 2.2 Research Methodology: Data Sources for Secondary Research

Figure 2.3 Research Methodology: Robust Quality Control

Figure 3.1 Market Dynamics: Forecast Methodology

Figure 3.2 Market Dynamics: Market Assessment Framework

Figure 4.1 Lessons Learnt from Past Recessions

Figure 5.1 Executive Summary: Overall Market Landscape

Figure 5.2 Executive Summary: Market Trends

Figure 5.3 Executive Summary: Market Forecast and Opportunity Analysis

Figure 6.1 Subsets of AI

Figure 6.2 Applications of AI in Healthcare

Figure 6.3 Advantages of Using AI in Drug Discovery

Figure 7.1 AI-based Drug Discovery Platform Providers: Distribution by Year of Establishment

Figure 7.2 AI-based Drug Discovery Platform Providers: Distribution by Company Size

Figure 7.3 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Region)

Figure 7.4 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Country)

Figure 7.5 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Business Model

Figure 7.6 Key AI-based Drug Discovery Platform Providers: Distribution by Platform Utilization

Figure 7.7 Key AI-based Drug Discovery Platform Providers: Distribution by Drug Discovery Stages Supported

Figure 7.8 Key AI-based Drug Discovery Platform Providers: Distribution by Type of AI Technology Used

Figure 7.9 Key AI-based Drug Discovery Platform Providers: Distribution by Target Therapeutic Area

Figure 7.10 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Molecule Analyzed

Figure 7.11 Key AI-based Drug Discovery Platform Providers: Distribution by End User

Figure 11.1 Partnerships and Collaborations: Quarterly Trend of Partnerships, Since Q1 2024

Figure 11.2 Partnerships and Collaborations: Distribution by Type of Partnership

Figure 11.3 Partnerships and Collaborations: Distribution by Quarter and Type of Partnership

Figure 11.4 Partnerships and Collaborations: Distribution by Type of Partner

Figure 11.5 Most Active Players: Distribution by Number of Partnerships

Figure 11.6 Partnerships and Collaborations: Local and International Agreements

Figure 11.7 Partnerships and Collaborations: Intracontinental and Intercontinental Agreements

Figure 12.1 Funding and Investments Analysis: Quarterly Trend of Funding, Since Q1 2024

Figure 12.2 Funding and Investments Analysis: Quarterly Trend of Amount Invested, Since Q1 2024 (USD Million)

Figure 12.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding

Figure 12.4 Funding and Investment Analysis: Distribution by Quarter and Type of Funding, Since Q1 2024

Figure 12.5 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)

Figure 12.6 Funding and Investment Analysis: Distribution of Amount Invested by Quarter and Type of Funding, Since Q1 2024

Figure 12.7 Funding and Investment Analysis: Distribution by Geography (Region)

Figure 12.8 Funding and Investment Analysis: Distribution by Geography (Country)

Figure 12.9 Most Active Players: Distribution by Number of Funding Instances

Figure 12.10 Most Active Players: Distribution by Amount Raised (USD Million)

Figure 12.11 Leading Investors: Distribution by Number of Funding Instances

Figure 13.1 Patent Analysis: Distribution by Type of Patent

Figure 13.2 Patent Analysis: Cumulative Distribution by Patent Publication Year, Since 2021

Figure 13.3 Patent Analysis: Distribution by Type of Patent and Publication Year, Since 2021

Figure 13.4 Patent Analysis: Distribution by Patent Application Year, Since 2018

Figure 13.5 Patent Analysis: Distribution by Patent Jurisdiction

Figure 13.6 Patent Analysis Distribution by CPC Symbols

Figure 13.7 Patent Analysis: Cumulative Distribution by Type of Applicant, Since 2021

Figure 13.8 Leading Industry Players: Distribution by Number of Patents

Figure 13.9 Leading Non-Industry Players: Distribution by Number of Patents

Figure 13.10 Leading Individual Assignees: Distribution by Number of Patents

Figure 13.11 Patent Benchmarking Analysis: Distribution of Patent Characteristics CPC Codes by Leading Industry Players

Figure 13.12 Patent Benchmarking Analysis: Distribution of Leading Industry Players by Patent Characteristics CPC Codes

Figure 13.13 Patent Analysis: Distribution by Patent Age

Figure 13.14 AI-based Drug Discovery: Patent Valuation

Figure 14.1 Key Elements of Porter's Five Forces

Figure 14.2 Threat of New Entrants

Figure 14.3 Bargaining Power of Buyers

Figure 14.4 Bargaining Power of Solution Providers

Figure 14.5 Threats of Substitute Products

Figure 14.6 Rivalry Among Existing Competitors

Figure 14.7 Porter's Five Forces Analysis: Concluding Remarks

Figure 17.1 Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035 (USD Billion)

Figure 17.2 Cost Saving Potential: Distribution by Application (USD Billion)

Figure 17.3 Cost Saving Potential in Target Identification/Validation, Till 2035 (USD Billion)

Figure 17.4 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)

Figure 17.5 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)

Figure 17.6 Cost Saving Potential: Distribution by Type of AI Technology (USD Billion)

Figure 17.7 Cost Saving Potential with Machine Learning, Till 2035 (USD Billion)

Figure 17.8 Cost Saving Potential with Molecular Modelling and Simulation, Till 2035 (USD Billion)

Figure 17.9 Cost Saving Potential with Deep Learning, Till 2035 (USD Billion)

Figure 17.10 Cost Saving Potential with Omics Integration, Till 2035 (USD Billion)

Figure 17.11 Cost Saving Potential with Generative Model, Till 2035 (USD Billion)

Figure 17.12 Cost Saving Potential with Structure-Based Drug Design, Till 2035 (USD Billion)

Figure 17.13 Cost Saving Potential with Other Technologies, Till 2035 (USD Billion)

Figure 17.14 Cost Saving Potential: Distribution by Therapeutic Area (USD Billion)

Figure 17.15 Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035 (USD Billion)

Figure 17.16 Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035 (USD Billion)

Figure 17.17 Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035 (USD Billion)

Figure 17.18 Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035 (USD Billion)

Figure 17.19 Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035 (USD Billion)

Figure 17.20 Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035 (USD Billion)

Figure 17.21 Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035 (USD Billion)

Figure 17.22 Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035 (USD Billion)

Figure 17.23 Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035 (USD Billion)

Figure 17.24 Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035 (USD Billion)

Figure 17.25 Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035 (USD Billion)

Figure 17.26 Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035 (USD Billion)

Figure 17.27 Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035 (USD Billion)

Figure 17.28 Cost Saving Potential: Distribution by End User (USD Billion)

Figure 17.29 Cost Saving Potential for Pharma and Biotech Companies, Till 2035 (USD Billion)

Figure 17.30 Cost Saving Potential for Contract Research Organizations (CRO), Till 2035 (USD Billion)

Figure 17.31 Cost Saving Potential for Research and Academic Institutions, Till 2035 (USD Billion)

Figure 17.32 Cost Saving Potential: Distribution by Geographical Regions (USD Billion)

Figure 17.33 Cost Saving Potential in North America, Till 2035 (USD Billion)

Figure 17.34 Cost Saving Potential in Europe, Till 2035 (USD Billion)

Figure 17.35 Cost Saving Potential in Asia-Pacific, Till 2035 (USD Billion)

Figure 17.36 Cost Saving Potential in MENA, Till 2035 (USD Billion)

Figure 17.37 Cost Saving Potential in Latin America, Till 2035 (USD Billion)

Figure 19.1 Global AI in Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 19.2 Global AI in Drug Discovery Market, Forecasted Estimates (Till 2035), Conservative Scenario (USD Million)

Figure 19.3 Global AI in Drug Discovery Market, Forecasted Estimates (Till 2035), Optimistic Scenario (USD Million)

Figure 20.1 AI in Drug Discovery Market: Distribution by Application

Figure 20.2 AI in Drug Discovery Market for Target Identification / Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 20.3 AI in Drug Discovery Market for Hit Generation / Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 20.4 AI in Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.1 AI in Drug Discovery Market: Distribution by Type of AI Technology

Figure 21.2 AI in Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.3 AI in Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.4 AI in Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.5 AI in Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.6 AI in Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.7 AI in Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 21.8 AI in Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 22.1 AI in Drug Discovery Market: Distribution by Drug Type

Figure 22.2 AI in Drug Discovery Market for Small Molecules, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 22.3 AI in Drug Discovery Market for Biologics, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 23.1 AI in Drug Discovery Market: Distribution by Deployment Mode

Figure 23.2 AI in Drug Discovery Market for Cloud-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 23.3 AI in Drug Discovery Market for On-premises Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 23.4 AI in Drug Discovery Market for SaaS-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.1 AI in Drug Discovery Market: Distribution by Therapeutic Area

Figure 24.2 AI in Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.3 AI in Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.4 AI in Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.5 AI in Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.6 AI in Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.7 AI in Drug Discovery Market for Immunological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.8 AI in Drug Discovery Market for Gastrointestinal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.9 AI in Drug Discovery Market for Endocrine Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.10 AI in Drug Discovery Market for Blood Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.11 AI in Drug Discovery Market for Ophthalmological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.12 AI in Drug Discovery Market for Dermatological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.13 AI in Drug Discovery Market for Infectious Diseases, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 24.14 AI in Drug Discovery Market for Urinary Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 25.1 AI in Drug Discovery Market: Distribution by End User

Figure 25.2 AI in Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 25.3 AI in Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 25.4 AI in Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 25.1 AI in Drug Discovery Market: Distribution by Geographical Regions

Figure 25.2 AI in Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.3 AI in Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.4 AI in Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.5 AI in Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.6 AI in Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.7 AI in Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.8 AI in Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.9 AI in Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.10 AI in Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.11 AI in Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.12 AI in Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.13 AI in Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.14 AI in Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.15 AI in Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.16 AI in Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.17 AI in Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.18 AI in Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.19 AI in Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.20 AI in Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.21 AI in Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.22 AI in Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.23 AI in Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.24 AI in Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.25 AI in Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Figure 26.26 Market Movement Analysis: Geographical Regions

Figure 26.27 Penetration-Growth (P-G) Matrix: Geographical Regions

Figure 27.1 Concluding Remarks: Current Market Landscape

Figure 27.2 Concluding Remarks: Partnerships and Collaborations

Figure 27.3 Concluding Remarks: Funding and Investments

Figure 27.4 Concluding Remarks: Patent Analysis

Figure 27.6 Concluding Remarks: Market Forecast and Opportunity Analysis (I/II)

Figure 27.7 Concluding Remarks: Market Forecast and Opportunity Analysis (II/II)

+ List of Tables

Table 7.1 AI-based Drug Discovery Platform Providers: Information on Year of Establishment, Company Size, and Location of Headquarters

Table 7.2 Key AI-based Drug Discovery Platform Providers: Information on Platform, Type of Business Model, Platform Utilization and Drug Discovery Stages Supported

Table 7.3 Key AI-based Drug Discovery Platform Providers: Information on Type of AI Technology Used

Table 7.4 Key AI-based Drug Discovery Platform Providers: Information on Target Therapeutic Area

Table 7.5 Key AI-based Drug Discovery Platform Providers: Information on Type of Molecule Analyzed and End User

Table 8.1 AI-based Drug Discovery Platform Providers based in North America: List of Companies Profiled

Table 8.2 Collaborations Pharmaceuticals: Company Overview

Table 8.3 Collaborations Pharmaceuticals: AI-based Drug Discovery Platform / Technology Portfolio

Table 8.4 Collaborations Pharmaceuticals: Recent Developments and Future Outlook

Table 8.5 Deep Genomics: Company Overview

Table 8.6 Deep Genomics: AI-based Drug Discovery Platform / Technology Portfolio

Table 8.7 Deep Genomics: Recent Developments and Future Outlook

Table 8.8 Genialis: Company Overview

Table 8.9 Genialis: AI-based Drug Discovery Platform / Technology Portfolio

Table 8.10 Genialis: Recent Developments and Future Outlook

Table 8.11 Insilico Medicine: Company Overview

Table 8.12 Insilico Medicine: AI-based Drug Discovery Platform / Technology Portfolio

Table 8.13 Insilico Medicine: Recent Developments and Future Outlook

Table 8.14 XtalPi: Company Overview

Table 8.15 XtalPi: AI-based Drug Discovery Platform / Technology Portfolio

Table 8.16 XtalPi: Recent Developments and Future Outlook

Table 9.1 AI-based Drug Discovery Platform Providers based in Europe: List of Companies Profiled

Table 9.2 BenevolentAI: Company Overview

Table 9.3 BenevolentAI: AI-based Drug Discovery Platform / Technology Portfolio

Table 9.4 Healx: Company Overview

Table 9.5 Healx: AI-based Drug Discovery Platform / Technology Portfolio

Table 9.6 Healx: Recent Developments and Future Outlook

Table 9.7 Iktos: Company Overview

Table 9.8 Iktos: AI-based Drug Discovery Platform / Technology Portfolio

Table 9.9 Iktos: Recent Developments and Future Outlook

Table 9.10 Optibrium: Company Overview

Table 9.11 Optibrium: AI-based Drug Discovery Platform / Technology Portfolio

Table 9.12 Optibrium: Recent Developments and Future Outlook

Table 10.1 AI-based Drug Discovery Platform Providers based in Asia-Pacific and Rest of World: List of Companies Profiled

Table 10.2 CytoReason: Company Overview

Table 10.3 CytoReason: AI-based Drug Discovery Platform / Technology Portfolio

Table 10.4 Deargen: Company Overview

Table 10.5 Deargen: AI-based Drug Discovery Platform / Technology Portfolio

Table 11.1 AI-based Drug Discovery: List of Partnerships and Collaborations

Table 12.1 AI in Drug Discovery Market: List of Funding and Investments

Table 13.1 Patent Analysis CPC Sections

Table 13.2 Patent Analysis Top CPC Symbols

Table 13.3 Patent Analysis Top CPC Codes

Table 13.4 Patent Analysis Summary of Benchmarking Analysis

Table 13.5 Patent Analysis Categorization based on Weighted Valuation Scores

Table 13.6 Patent Portfolio List of Leading Patents by Highest Relative Valuation

Table 13.7 Patent Portfolio List of Leading Patents by Number of Citations

Table 15.1 Company Valuation Analysis: Scoring Sheet

Table 15.2 Company Valuation Analysis: Estimated Valuation

Table 18.1 Growth Drivers: Market Impact and Time Period

Table 18.2 Growth Restraints: Market Impact and Time Period

Table 18.3 Growth Opportunities: Market Impact and Time Period

Table 18.4 Growth Challenges: Market Impact and Time Period

Table 26.1 Aigenpulse: Company Snapshot

Table 26.2 Cloud Pharmaceuticals: Company Snapshot

Table 26.3 Deargen: Company Snapshot

Table 26.4 Intelligent Omics: Company Snapshot

Table 26.5 Optibrium: Company Snapshot

Table 26.6 Pepticom: Company Snapshot

Table 26.7 Sage-N Research: Company Snapshot

Table 27.1 AI-based Drug Discovery Platform Providers: Distribution by Year of Establishment

Table 27.2 AI-based Drug Discovery Platform Providers: Distribution by Company Size

Table 27.3 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Region)

Table 27.4 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Country)

Table 27.5 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Business Model

Table 27.6 Key AI-based Drug Discovery Platform Providers: Distribution by Platform Utilization

Table 27.7 Key AI-based Drug Discovery Platform Providers: Distribution by Drug Discovery Stages Supported

Table 27.8 Key AI-based Drug Discovery Platform Providers: Distribution by Type of AI Technology Used

Table 27.9 Key AI-based Drug Discovery Platform Providers: Distribution by Target Therapeutic Area

Table 27.10 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Molecule Analyzed

Table 27.11 Key AI-based Drug Discovery Platform Providers: Distribution by End User

Table 27.12 Partnerships and Collaborations: Quarterly Trend of Partnerships, Since Q1 2024

Table 27.13 Partnerships and Collaborations: Distribution by Type of Partnership

Table 27.14 Partnerships and Collaborations: Distribution by Quarter and Type of Partnership

Table 27.15 Partnerships and Collaborations: Distribution by Type of Partner

Table 27.16 Most Active Players: Distribution by Number of Partnerships

Table 27.17 Most Active Players: Distribution by Number of Partnerships

Table 27.18 Partnerships and Collaborations: Local and International Agreements

Table 27.19 Partnerships and Collaborations: Intracontinental and Intercontinental Agreements

Table 27.20 Funding and Investments Analysis: Quarterly Trend of Funding, Since Q1 2024

Table 27.21 Funding and Investments Analysis: Quarterly Trend of Amount Invested, Since Q1 2024 (USD Million)

Table 27.22 Funding and Investments: Distribution of Instances by Type of Funding

Table 27.23 Funding and Investments: Distribution by Quarter and Type of Funding, Since Q1 2024 (USD Million)

Table 27.24 Funding and Investments: Distribution of Amount Invested by Type of Funding (USD Million)

Table 27.25 Funding and Investment Analysis: Distribution of Amount Invested by Quarter and Type of Funding, Since Q1 2024

Table 27.26 Funding and Investments: Distribution by Geography (Region)

Table 27.27 Funding and Investments: Distribution by Geography (Country)

Table 27.28 Most Active Players: Distribution by Number of Funding Instances

Table 27.29 Most Active Players: Distribution by Amount Raised (USD Million)

Table 27.30 Leading Investors: Distribution by Number of Funding Instances

Table 27.31 Patent Analysis: Distribution by Type of Patent

Table 27.32 Patent Analysis: Cumulative Distribution by Patent Publication Year, Since 2021

Table 27.33 Patent Analysis: Distribution by Type of Patent and Patent Publication Year, Since 2021

Table 27.34 Patent Analysis: Distribution by Patent Application Year, Since 2018

Table 27.35 Patent Analysis: Distribution by Patent Jurisdiction

Table 27.36 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant, Since 2021

Table 27.37 Leading Industry Players: Distribution by Number of Patents

Table 27.38 Leading Non-Industry Players: Distribution by Number of Patents

Table 27.39 Leading Individual Assignees: Distribution by Number of Patents

Table 27.40 Patent Analysis: Distribution by Patent Age

Table 27.41 Patent Analysis: Distribution by Patent Valuation

Table 27.42 Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035 (USD Billion)

Table 27.43 Cost Saving Potential: Distribution by Application (USD Billion)

Table 27.44 Cost Saving Potential in Target Identification/Validation, Till 2035 (USD Billion)

Table 27.45 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)

Table 27.46 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)

Table 27.47 Cost Saving Potential: Distribution by Type of AI Technology (USD Billion)

Table 27.48 Cost Saving Potential with Machine Learning, Till 2035 (USD Billion)

Table 27.49 Cost Saving Potential with Molecular Modelling and Simulation, Till 2035 (USD Billion)

Table 27.50 Cost Saving Potential with Deep Learning, Till 2035 (USD Billion)

Table 27.51 Cost Saving Potential with Omics Integration, Till 2035 (USD Billion)

Table 27.52 Cost Saving Potential with Generative Model, Till 2035 (USD Billion)

Table 27.53 Cost Saving Potential with Structure-Based Drug Design, Till 2035 (USD Billion)

Table 27.54 Cost Saving Potential with Other Technologies, Till 2035 (USD Billion)

Table 27.55 Cost Saving Potential: Distribution by Therapeutic Area (USD Billion)

Table 27.56 Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035 (USD Billion)

Table 27.57 Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035 (USD Billion)

Table 27.58 Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035 (USD Billion)

Table 27.59 Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035 (USD Billion)

Table 27.60 Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035 (USD Billion)

Table 27.61 Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035 (USD Billion)

Table 27.62 Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035 (USD Billion)

Table 27.63 Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035 (USD Billion)

Table 27.64 Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035 (USD Billion)

Table 27.65 Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035 (USD Billion)

Table 27.66 Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035 (USD Billion)

Table 27.67 Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035 (USD Billion)

Table 27.68 Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035 (USD Billion)

Table 27.69 Cost Saving Potential: Distribution by End User (USD Billion)

Table 27.70 Cost Saving Potential for Pharma and Biotech Companies, Till 2035 (USD Billion)

Table 27.71 Cost Saving Potential for Contract Research Organizations (CRO), Till 2035 (USD Billion)

Table 27.72 Cost Saving Potential for Research and Academic Institutions, Till 2035 (USD Billion)

Table 27.73 Cost Saving Potential: Distribution by Geographical Regions (USD Billion)

Table 27.74 Cost Saving Potential in North America, Till 2035 (USD Billion)

Table 27.75 Cost Saving Potential in Europe, Till 2035 (USD Billion)

Table 27.76 Cost Saving Potential in Asia-Pacific, Till 2035 (USD Billion)

Table 27.77 Cost Saving Potential in MENA, Till 2035 (USD Billion)

Table 27.78 Cost Saving Potential in Latin America, Till 2035 (USD Billion)

Table 27.79 Global AI in Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.80 Global AI in Drug Discovery Market, Forecasted Estimates (Till 2035), Conservative Scenario (USD Million)

Table 27.81 Global AI in Drug Discovery Market, Forecasted Estimates (Till 2035), Optimistic Scenario (USD Million)

Table 27.82 AI in Drug Discovery Market: Distribution by Application

Table 27.83 AI in Drug Discovery Market for Target Identification/Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.84 AI in Drug Discovery Market for Hit Generation/Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.85 AI in Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.86 AI in Drug Discovery Market: Distribution by Type of AI Technology

Table 27.87 AI in Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.88 AI in Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.89 AI in Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.90 AI in Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.91 AI in Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.92 AI in Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.93 AI in Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.94 AI in Drug Discovery Market: Distribution by Drug Type

Table 27.95 AI in Drug Discovery Market for Small Molecules, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.96 AI in Drug Discovery Market for Biologics, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.97 AI in Drug Discovery Market: Distribution by Deployment Mode

Table 27.98 AI in Drug Discovery Market for Cloud-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.99 AI in Drug Discovery Market for On-premises Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.100 AI in Drug Discovery Market for SaaS-based Deployment, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.101 AI in Drug Discovery Market: Distribution by Therapeutic Area

Table 27.102 AI in Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.103 AI in Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.104 AI in Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.105 AI in Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.106 AI in Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.107 AI in Drug Discovery Market: Distribution by End User

Table 27.108 AI in Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.109 AI in Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.110 AI in Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.111 AI in Drug Discovery Market: Distribution by Geographical Regions

Table 27.112 AI in Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.113 AI in Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.114 AI in Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.115 AI in Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.116 AI in Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.117 AI in Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.118 AI in Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.119 AI in Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.120 AI in Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.121 AI in Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.122 AI in Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.123 AI in Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.124 AI in Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.125 AI in Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.126 AI in Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.127 AI in Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.128 AI in Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.129 AI in Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.130 AI in Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.131 AI in Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.132 AI in Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.133 AI in Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.134 AI in Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

Table 27.135 AI in Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

AI In Drug Discovery Market Size, Share And Growth Trend Analysis Report By Region And Segmentation Forecasts (2026 - 2035)