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The discovery and identification of novel drug candidates is a time-intensive process, which is fraught with several challenges. One of the main concerns associated with the drug development process is the high attrition rate, which is often linked to the trial-and-error method adopted for lead identification. In this context, only a small percentage of pharmacological leads are eventually translated into potential candidates for clinical studies. Further, of these candidates, nearly 90% are unable to advance further in the development process. This, in turn, leads to a significant loss for drug developers, in terms of both resources and finances. Usually, a prescription drug requires at least 10 years to reach the market, and an average investment of over USD 2 billion. In addition, it is reported that the drug discovery phase accounts for about one-third of the aforementioned costs. In recent years, artificial intelligence (AI) has emerged as prominent tool, demonstrated to have the potential to address a number of existing challenges. As a result, players engaged in the pharmaceutical domain have started implementing AI based tools to better inform their drug discovery and development operations, using available chemical and biological data.
Currently, a number of AI-based techniques, including machine learning, deep learning, supervised learning, unsupervised learning and natural language processing are being used across various stages of the drug development process. Specifically, AI-based solutions are being extensively used in combination with deep learning algorithms to produce actionable insights for target identification, hit generation, as well as lead optimization. Such solutions are anticipated to increase the overall R&D productivity and reduce clinical failure of product candidates. Moreover, estimates suggest that, in 2022, the adoption of AI-based solutions for drug discovery are likely to enable savings worth USD 8.57 billion, with market projections suggesting cost savings of USD more than 28 billion by 2035. Despite the fact that niche startups are spearheading the innovation in this domain, several big pharma players are also actively acquiring capabilities for these technologies. Numerous tech giants, such as Google, IBM and Microsoft, have either developed their proprietary products or are offering solutions through collaborations with other industry stakeholders; for instance Google’s DeepMind and IBM Watson. Even though only a few of such AI-based platforms have gone public, developers have experienced considerable growth in share value as their respective platforms / product candidates progressed through the various stages of development. Taking into consideration both the historical and contemporary scenarios, we believe that the AI-based Drug Discovery Market presents lucrative investment opportunities for both short- and long-term investors.
Scope of the Report
The Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market (Focus on Need for AI-based Drug Discovery, Market Landscape of Key Players, Analysis of Product Offerings and Affiliated Value Propositions, Insights from Historical Funding Activity, Startup Health Indexing, Potential Venture Opportunities, Financial Analysis of Key Public Ventures, Market Forecast and Opportunity Analysis, Insights from Publicly Disclosed Investor Exits and Key Acquisition Targets) report provides detailed information on the AI-based Drug Discovery Market, along with a focus on drug discovery platforms, service and technology providers. It offers a technical and financial perspective on how the opportunity in this domain is likely to evolve, in terms of future business success, over the coming decade. The information in this report has been presented across multiple deliverables, featuring MS Excel sheets (some of which include interactive elements) and an MS PowerPoint deck, which summarizes the key takeaways from the project, and insights drawn from the curated data. The report features the following details:
One of the key objectives of the report was to evaluate the current opportunity and the future potential of AI-based drug discovery over the coming decades. We have provided an informed estimate of the likely evolution of the market in the short to mid-term and long term, during the period 2021-2035. The opportunity has been segregated on the basis of [A] Drug discovery steps (target identification / validation, hit generation / lead identification, lead optimization), [B] 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 Geographies (North America, Europe, Asia-Pacific, Latin America, MENA, and RoW). In order to account for future uncertainties in the market and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution.
Spreadsheet I includes details on the key players captured in the report, along with their respective products. It also features proprietary company health indexing analysis, value proposition analysis, inputs for a detailed company competitiveness analysis and a shortlist of industry stakeholders that were deemed to be likely targets of mergers / acquisitions, in the near future. The deliverable includes a summary dashboard, featuring interactive graphical representations of key insights generated from the collated data.
Spreadsheet II features a summary dashboard, including interactive graphical representations of some of the key insights generated related to the capital investments made in AI-based drug discovery domain (since 2011).
Spreadsheet III showcases our proprietary analysis to identify the venture opportunities, wherein we have developed the means to extrapolate publicly available information (related to company portfolio information and opportunity analysis) to develop informed estimates of the companies likely to provide a high return on investments.
Spreadsheet IV is a collection of multiple MS Excel sheets that provide summaries of insights generated from a detailed fundamental and technical financial analysis, of publicly listed ventures in the key players dataset.
Spreadsheet V offers our independent perspective on the various types of risks (namely operations-related risks, business-related risks, financial / asset-related risks, product / technology risks, and other risks) that the publicly listed ventures are presently exposed to; it includes a summary heat map representation that provides a pictorial perspective of the diversity and level of risks (as per our opinion), as well.
Spreadsheet VI is a summary MS Excel dashboard, offering detailed graphical representations of the contemporary and future opportunity associated with AI-based drug discovery domain.
Spreadsheet VII includes publicly available information on the investments made by select investors in companies that are now publicly listed. Based on the aforementioned data, we have offered a perspective on likely returns on investment received by the mentioned investors.
Chapter 1 provides a brief summary of the content presented in the report, beginning with the need for AI-based drug discovery. It goes on to discuss some of the key benefits of these discovery platforms and their advantages over other conventional approaches. Finally, the chapter provides an overview of the current scenario, offering a perspective on how, in light of recent funding activity, the market is likely to evolve over the coming years.
Chapter 2 and 3 feature brief (pictorial) summaries of the key objectives and approach used for data collection and analysis, in this study.
Chapter 4 features an executive summary of the key insights generated from the data and analytical outputs presented in the report.
Section I: Need for AI-based Drug Discovery & Market Landscape
Chapter 5 describes the current need for AI in Drug Discovery and highlights key facts about the origin and development of such platforms. It features information on current areas of innovation, along with the opinions of experts, describing the various benefits of these approaches, and anticipated future challenges. It includes information on some of the key players that are engaged in this domain, along with examples of ventures that have either succeeded or failed in the market. The views presented in this chapter are backed by inputs from representatives from key stakeholder companies in this domain (as stated in publicly available articles and interview transcripts). It concludes with information on the different conferences that have been conducted in this domain in the recent past, and those that are planned for the near future.
Chapter 6 focuses on some of the key players (companies established on or after 2006 and features detailed analysis of the aforementioned companies. It highlights important company related details, such as year of establishment, headquarters, company size, and type of venture.
Chapter 7 includes information on the various products and affiliated services offered by the companies captured in the report (listed in Chapter 6). It also features analysis based on number and type of product. Based on the aforementioned insights and details presented in Chapter 6, we have developed a quantitative perspective on the relative health (based on basic company details, product details, financing activity, and estimated revenues and profits) of the captured companies.
Chapter 8 offers an informed perspective on the apparent value proposition of the companies captured in the report (listed in Chapter 6). Based on multiple relevant inputs (as inferred from publicly disclosed value statements), namely, treatment-related value offered, value to patients and technology related value, we developed an empirical framework to quantify the value proposition of a business.
Chapter 9 features a detailed company competitiveness analysis, offering a quantitative basis for comparing the developed of diverse cell and gene therapies captured in this report. It is worth mentioning that the analysis described in this section is based on a proprietary scoring criteria, which was informed via our company health indexing and valuation exercise.
Section II Analysis of Investments
Chapter 10 offers insights from a detailed analysis of the funding and investment activity in this domain, since 2011. It includes financing category-wise trends, describing the relative maturity (in terms of number of funding instances and total capital raised) of the key companies discussed in the report.
Section III Financial Analysis & Assessment of Business Risks
Chapter 11 is modelled in the likeness of an equity research report. It features a review of the overall AI-based Drug Discovery Market from a financial perspective and includes detailed fundamental (insights from the balance sheet, and key financial ratios) and technical analyses (insights from historical and recent stock price variations, and analysis using popular stock performance indicators) of financial data of 16 of the publicly listed companies within the market landscape dataset.
Chapter 12 includes a business risk analysis, offering insights encompassing several known categories of risk; these include operations-related risks, business-related risks, product / technology risks, financial / asset-related risks, and other risks.
Section IV Market Forecast & Opportunity Analysis
Chapter 13 features an insightful market forecast analysis, highlighting the estimated current and future sizes of overall AI-based Drug Discovery Market till the year 2035. The opportunity has been segregated on the basis of [A] Drug discovery steps (target identification / validation, hit generation / lead identification, lead optimization), [B] 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 Geographies (North America, Europe, Asia-Pacific, Latin America, MENA, and RoW).
Section V Analysis of Returns on Investment, Key Acquisition Targets and Potential Venture Opportunities
Chapter 14 includes case studies of instances where investors have exited various AI-based drug discovery-related ventures, offering insights on returns on investment made (based on availability of data). The abovementioned estimates / details, offer a perspective on how past investments have paid off for investors as companies gradually went public, over time.
Chapter 15 offers insights from a proprietary analysis that leverages inputs from the startup health indexing and value proposition analysis (described in Section I), to offer qualitative recommendations on companies that are likely to be perceived as key acquisition targets.
Chapter 16 features a proprietary basis for identifying players which are likely to be potential venture opportunities for the investors, based on the market trends. The analysis is driven by the expected growth rates of the segments coupled with competition from the existing players in each segment.
Chapter 17 provides a pictorial summary of the overall project.
Chapter 18 is a set of appendices.
1. KEY PLAYERS AND PRODUCTS DATASET
1.1. Analysis Notes
1.2. Summary Dashboard
1.3. Market Landscape Dataset
1.4. Product Landscape Dataset
1.5. Company Health Indexing
1.6. Value Proposition Analysis
1.7. Key Acquisition Targets
A1 – A8Appendices
2. FUNDING AND INVESTMENT ANALYSIS
2.1. Analysis Notes
2.2. Summary Dashboard
2.3. Capital Investments in AI-based Drug Discovery
A1 – A2Appendices
3. POTENTIAL INVESTMENT OPPORTUNITIES
3.1. Market Forecast (Input Data)
3.2. Leading Business Segments
3.3. Optimal Investment Targets
4. FUNDAMENTAL AND TECHNICAL FINANCIAL ANALYSIS
The information presented in this analysis is spread across separate MS Excel sheets, which provide information on key financial parameters and competitive insights based on historical and prevalent trends.
5. BUSINESS RISK ANALYSIS
5.1. Analysis Notes
5.2. Business Risk Data
A1 – A2Appendices
6. MARKET FORECAST AND OPPORTUNITY ANALYSIS
6.1. Analysis Notes
6.2. Summary Dashboard
6.3. Market Forecast and Opportunity Analysis
A1 – A2Appendices
7. RETURNS ON INVESTMENT
7.1. Analysis Notes
7.2. Estimated RoI for Investors in Company A
7.3. Estimated RoI for Investors in Company B
7.4. Estimated RoI for Investors in Company C
7.5. Estimated RoI for Investors in Company D
7.6. Estimated RoI for Investors in Company E
7.7. Estimated RoI for Investors in Company F
7.8. Estimated RoI for Investors in Company G
7.9. Estimated RoI for Investors in Company H
7.10. Estimated RoI for Investors in Company I
7.11. Estimated RoI for Investors in Company J
A1 – A2Appendices
2. Project Approach
3. Project Objectives
4. Executive Summary
Section I: Need for AI-based Drug Discovery Market and Market Landscape
5. AI-BASED DRUG DISCOVERY MARKET
5.2. Types of AI
5.3. Developmental History
5.4. Key Players
5.5. Benefits of AI in Drug Discovery
5.6. Challenges Related to AI in Drug Discovery
5.7. Contemporary Sentiments and Expert Opinions
6. Market Landscape
6.1. Key Players in the AI in Drug Discovery Market
6.2. Analysis of Competitive Landscape
7. Product Landscape and Company Health Indexing
7.1. List of Products
7.2. Analysis of Product Landscape
7.3. Health Indexing Methodology
7.4. Company Health Indexing
8. Value Proposition Analysis
8.1. Overview and Methodology
8.2. Intellectual Capital Related Value
8.3. Developer Value
8.4. Platform Related Value
8.5. Value to End User
9. Company Competitiveness Analysis
9.1. Overview and Methodology
9.2. Company Competitiveness Analysis
9.3. Concluding Remarks
Section II Analysis of Investments
10. Funding and Investments Analysis
10.2. Analysis by Type of Funding
10.3. Analysis by Geography
10.4. Most Active Players and Popular Investors
10.5. Analysis of Trends Associated with Individual Funding Categories
10.6. Funding and Investments Summary
Section III Financial Analysis and Assessment of Business Risks
11. Financial Analysis of Public Ventures
11.1. Fundamental Financial Analysis Overview
11.2. Financial Ratios (Interpretation Guide)
11.3. Case Study 1
11.4. Technical Financial Analysis Overview
11.5. Technical Indicators (Interpretation Guide)
11.6. Case Study 2
11.7. Company Profiles
12. Business Risk Analysis
12.1. Overview and Methodology
12.2. Operations-related Risks
12.3. Business-related Risks
12.4. Financial / Asset-related Risks
12.5. Product / Technology Risks
12.6. Other Risks
12.8. Business Risks Summary
Section IV Market Forecast and Opportunity Analysis
13. Market Forecast and Opportunity Analysis
13.1. Overview and Methodology
13.2. Global AI in Drug Discovery Market, 2022 - 2035
13.2.1. Analysis by Drug Discovery Steps
13.2.2. Analysis by Geography
13.2.3. Analysis by Therapeutic Area
13.2.4. Concluding Remarks
Section V Analysis of Returns on Investment, Key Acquisition Targets and Promising Investment Opportunities
14. Analysis of Returns on Investment
14.1. Overview and Methodology
14.2. Case Studies
14.3. Concluding Remarks
15. Key Acquisition Targets
15.2. List of Key Acquisition Targets
15.3. Concluding Remarks
16. Promising Investments Analysis
16.1. Overview and Methodology
16.2. Leading Business Segments
16.3. Concluding Remarks
The following companies / institutes / government bodies and organizations have been mentioned in this report.
Source 1: https://www.sciencedirect.com/science/article/pii/S2211383522000521
Source 2: http://phrma-docs.phrma.org/sites/default/files/pdf/rd_brochure_022307.pdf