Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market

INTRODUCTION

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 AI-based Drug Discovery Market" 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:

• A qualitative and quantitative (wherever information was available) perspective on the current need for AI in the drug discovery domain. It presents details on the key applications of AI in drug discovery, along with information on the benefits of using such methodologies over conventional discovery approaches. Further, it highlights various challenges faced during various stages of drug discovery, and the opinions of representatives from key stakeholder companies involved in this domain.
• A detailed analysis of AI-based drug discovery focused companies that were established post 2005, featuring inputs on observed trends related to basic input parameters, such as year of establishment, location of headquarters, company size, and type of venture.
• A quantitative perspective on the relative health (based on basic company details, product details, financing activity, and estimated revenues and profits) of companies that have been described in detail in this report. This analysis is based on a proprietary scoring criterion, which was informed via secondary research.
• An assessment of the various products and affiliated services, offered by the companies mentioned above, featuring analysis based on number and types of services / platforms, and an informed perspective on the value of the aforementioned offerings based on multiple relevant aspects, namely intellectual capital related value, value to end users, developer value, and others.
• A company competitiveness analysis, which offers a quantitative basis for comparing the strengths / contributions of various industry stakeholders that are involved in providing AI-based services and platforms for drug discovery, captured in this report. It is worth mentioning that this analysis is based on the insights generated from the abovementioned relative health indexing and value proposition analyses.
• A detailed analysis of the funding and investment activity that has taken place in this domain, since 2011. It also 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. Further, it features a list of the leading investors in AI in drug discovery market, based on their participation in financing activity in this industry segment.
• An elaborate review of the overall AI-based Drug Discovery Market from a financial perspective, including 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 the publicly listed companies within the market landscape dataset.
• A business risk analysis, focused on some of the major categories of risk that are usually discussed in the industry, namely operations-related risks, overall business-related risks, financial risks, product / technology associated risks, and social, economic, environmental and political risks.
• 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). Leveraging the abovementioned details, the report offers an informed opinion on the future outlook for investors in the AI-based Drug Discovery Market.
• A key acquisition targets analysis, based on the insights generated during the course of this study, highlighting some of the promising early-to-mid stage business ventures around which there is likely to be interest for future acquisitions / mergers.

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.

RESEARCH METHODOLOGY

The data presented in this report has been gathered via secondary research and analyzed using proprietary methods / tools, in order to develop a detailed perspective on the market landscape and the associated opportunity, across different global regions. As much as possible, the collated data has been checked for accuracy from multiple, reliable sources of information.

The secondary sources of information include:

• Company' websites
• Annual reports & SEC filings
• Investor presentations
• Industry specific equity research reports
• Industry databases
• Press releases
• Industry analysts' views (as expressed on related news articles and other informative publications)

The insights presented are solely based on our knowledge, research and understanding of the relevant market, as inferred publicly available sources of information.

CHAPTER OUTLINES

Excel Deliverables

• 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.

PowerPoint Deliverable

• 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.