AI in Drug Discovery Market Overview
The AI in drug discovery market is estimated to be worth $0.74 billion in 2022 and is expected to grow at compounded annual growth rate (CAGR) of 25% during the forecast period. The discovery and development process of a novel therapeutic candidate is often tedious and fraught with several challenges. The key concern associated with the overall process is the high attrition rate, which is often attributed to the trial-and-error approach followed for the drug discovery process. In fact, only a small proportion of pharmacological leads are translated into viable product candidates for clinical studies. In addition, experts believe that close to 90% of the product candidates considered in such studies fail to advance further in the development process. This, in turn, often results in a massive financial burden. In this context, it is estimated that a prescription drug takes around 10 to 15 years and an average investment of USD 1 to 2 billion, in order to traverse from the bench to the market. Moreover, around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone. Therefore, to address the existing concerns, such as rising capital requirements and failure of late-stage programs, pharmaceutical players are currently exploring the implementation of Artificial Intelligence (AI) based tools to better inform their discovery and development operations, using available chemical and biological data. Specifically, AI drug discovery is believed to be capable of processing and analyzing large volumes of clinical / medical data, as well as leverage it to better inform modern drug discovery efforts. In this context, deep learning algorithms have been demonstrated to be able to cross-reference published scientific literature (structured data) with electronic health records (EHRs) and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization.
At present, key AI-based tools, such as deep learning, supervised learning, unsupervised learning and natural language processing and machine learning, are extensively being deployed for drug discovery within the healthcare sector. The use of machine learning drug discovery is expected to not only improve the overall R&D productivity, but also reduce clinical failure of product candidates, by enabling accurate prediction of its safety and efficacy during early stages of development. Close to 210 AI drug discovery companies currently claim to offer AI-based services, platforms and tools for drug discovery. Further, over USD 10 billion has been invested in this market by both private and public sector investors, in the last five years. Interestingly, close to 50% of the aforementioned amount was invested in the last two years, reflecting the increasing interest of stakeholders in AI-based tools for drug discovery. Additionally, close to 440 recently instances of collaborations have been reported between industry / academic stakeholders in order to advance the development of various AI-based solutions for drug discovery. Considering the active initiatives being undertaken by AI drug discovery companies in AI in drug discovery market, we are led to believe that the opportunity for stakeholders in this niche, albeit upcoming, industry is likely to witness a commendable market growth during the forecast period.
Key AI Drug Discovery Companies
Examples of key AI drug discovery companies engaged in AI in drug discovery market (which have also been profiled in this market report; the complete list of companies is available in the full report) include Aiforia Technologies, Atomwise, BioSyntagma, Chemalive, Collaborations Pharmaceuticals, Cyclica, DeepMatter, Exscientia, InveniAI, MAbSilico, Optibrium, Recursion Pharmaceuticals, Sensyne Health and Valo Health. This market report includes an easily searchable excel database of all the AI drug discovery companies providing AI drug discovery services, worldwide.
Recent Developments in AI in Drug Discovery Market:
Several recent developments have taken place in the field of AI in drug discovery market. We have outlined some of these recent initiatives below. These developments, even if they took place post the release of our market report, substantiate the overall market trends that have been outlined in our analysis.
- In July 2023, Aiforia entered into a collaboration with Orion for the development of AI-based image analysis solutions for preclinical research and product development.
- In July 2023, NVIDIA announced the investment of USD 50 million in Recursion Pharmaceuticals with the aim to create artificial intelligence assisted drug discovery models.
- In May 2023, Google launched AI-powered tools, namely Multiomics Suite and Target and Lead Identification Suite, to accelerate drug discovery in the field of precision medicine.
Scope of the Report
The ‘AI in Drug Discovery Market (2nd Edition): Distribution by Drug Discovery Steps (Target Identification / Validation, Hit Generation / Lead Identification and Lead Optimization), Therapeutic Area (Oncological Disorders, CNS Disorders, Infectious Diseases, Respiratory Disorders, Cardiovascular Disorders, Endocrine Disorders, Gastrointestinal Disorders, Musculoskeletal Disorders, Immunological Disorders, Dermatological Disorders and Others) and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA and Rest of the World): Industry Trends and Global Forecasts, 2022-2035’ market report features an extensive study of the current market landscape, market size, market forecast and future opportunities for the AI drug discovery companies involved engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The market research report features an in-depth analysis, highlighting the capabilities of AI-based drug discovery service / technology providers. Amongst other elements, the market report features:
- A detailed overview of the overall landscape of AI drug discovery companies offering AI-based services, platforms and tools for drug discovery, along with information on several relevant parameters, such as their year of establishment, company size (in terms of employee count), location of headquarters (North America, Europe, Asia-Pacific and Rest of the World) and type of company (service providers, technology providers and in-house players). The chapter also covers details related to the type of AI technology (artificial intelligence (undefined), deep learning, machine learning drug discovery (undefined), natural language processing, data science, reinforcement learning, supervised learning and unsupervised learning), drug discovery steps (target discovery / identification / validation, lead identification / optimization / generation and ADME / toxicity testing), type of drug molecule (small molecules, biologics and both) and target therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, rare diseases, metabolic disorders, respiratory disorders, gastrointestinal disorders, musculoskeletal disorders, dermatological disorders, hematological disorders, ophthalmic disorders and other disorders).
- Elaborate profiles of prominent AI drug discovery companies (shortlisted based on a proprietary criterion) engaged in AI in drug discovery market, across North America, Europe and Asia-Pacific. Each profile provides an overview of the company, featuring information on the year of establishment, number of employees, location of their headquarters, key executives, details related to its AI-based drug discovery technology portfolio, recent developments and an informed future outlook.
- An analysis of partnerships inked between stakeholders engaged in AI in drug discovery market, during the period 2009-2022, covering research and development agreements, technology access / utilization agreements, acquisitions, technology licensing agreements, joint ventures / mergers, technology integration agreements, service agreements and other related agreements. Further, the partnership activity in this domain has been analyzed based on various parameters, such as year of partnership, type of partnership, target therapeutic area, focus area, type of partner company and most active AI drug discovery companies (in terms of number of partnerships). It also highlights the regional distribution of the partnership activity witnessed in this market.
- A detailed analysis of various investments, such as grants, awards, seed financing, venture capital financing, debt financing, capital raised from IPOs and subsequent offerings, that were undertaken by AI drug discovery companies engaged in AI in drug discovery market, during the period 2006-2022.
- An in-depth analysis of the various patents that have been filed / granted related to AI-based drug discovery technologies, from 2019 to February 2022, taking into consideration parameters, such as application year, geographical region, CPC symbols, emerging focus areas, type of applicant and leading players (in terms of size of intellectual property portfolio). It also includes a patent benchmarking analysis and a detailed valuation analysis.
- A qualitative analysis, highlighting the five competitive forces prevalent in AI in drug discovery market, including threats for new entrants, bargaining power of drug developers, bargaining power of AI-based drug discovery companies, threats of substitute technologies and rivalry among existing competitors.
- An elaborate valuation analysis of AI drug discovery companies that are involved in the AI in drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
- An insightful analysis highlighting the likely cost saving potential associated with the use of AI in the drug discovery sector, based on information gathered from close to 15 countries, taking into consideration various parameters, such as pharmaceutical R&D expenditure, drug discovery expenditure / budget and adoption of AI across various drug discovery steps.
One of the key objectives of this market report is to provide a detailed market forecast analysis in order to estimate the existing market size and future opportunity for AI in Drug Discovery Market during the forecast period. We have provided informed estimates of the likely evolution of the market in the short to mid-term and long term, for the forecast period 2022-2035. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as [A] drug discovery steps (target identification / validation, hit generation / lead identification and lead optimization), [B] target therapeutic area (oncological disorders, CNS disorders, infectious diseases, respiratory disorders, cardiovascular disorders, endocrine disorders, gastrointestinal disorders, musculoskeletal disorders, immunological disorders, dermatological disorders and others) and [C] key geographical regions (North America, Europe, Asia-Pacific, MENA, Latin America and Rest of the world). To account for future uncertainties in the market and to add robustness to our model, we have provided three market forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution.
The opinions and insights presented in the market report were also influenced by discussions held with senior stakeholders in the industry. The market research report features detailed transcripts of interviews held with the following individuals:
- Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse)
- Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals)
- Bo Ram Beck (Head Researcher, DEARGEN)
- Simon Haworth (Chief Executive Officer, Intelligent Omics)
- Immanuel Lerner (Chief Executive Officer and Co-Founder, Pepticom)
Frequently Asked Questions
Question 1: How is AI changing drug discovery? Answer:
AI is being used for processing and analyzing large volumes of clinical / medical data, as well as leverage it to better inform modern drug discovery efforts. It can integrate scientific literature (structured data) with electronic health records (EHRs) and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization.
Question 2: How big is the AI in drug discovery market? Answer:
The AI in drug discovery market size is estimated to be worth $0.74 billion in 2022.
Question 3: What is the projected market growth of the AI in drug discovery market? Answer:
The AI in drug discovery market is expected to grow at compounded annual growth rate (CAGR) of 25% during the forecast period.
Question 4: Who are the leading AI drug discovery companies in the AI in drug discovery market? Answer:
Examples of key AI drug discovery companies engaged in AI in drug discovery market (which have also been profiled in this market report; the complete list of companies is available in the full report) include Aiforia Technologies, Atomwise, BioSyntagma, Chemalive, Collaborations Pharmaceuticals, Cyclica, DeepMatter, Exscientia, InveniAI, MAbSilico, Optibrium, Recursion Pharmaceuticals, Sensyne Health and Valo Health.
Question 5: How many companies are currently engaged in AI in drug discovery market? Answer:
Close to 210 AI drug discovery companies currently claim to offer AI-based services, platforms and tools for drug discovery.
Question 6: How much money has been invested by stakeholders in the AI in drug discovery market? Answer:
Over USD 10 billion has been invested in the AI in drug discovery market by both private and public sector investors, in the last five years.
Question 7: Which region is the hub for ai drug discovery companies engaged in the AI in drug discovery market? Answer:
North America emerged as the hub for ai drug discovery companies engaged in the AI in drug discovery market, with 55% of the players established in the region.
Question 8: What are some key AI-based tools employed in the AI in drug discovery market? Answer:
Machine learning, deep learning, supervised learning, unsupervised learning and natural language processing are some of the key AI-based tools being deployed in AI in drug discovery market.