AI in Drug Discovery Fundamental Analysis

AI in Drug Discovery Overview

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:

• 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.