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Savings of $8.57 billion thanks to AI

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Dublin, Jan. 04, 2023 (GLOBE NEWSWIRE) — The report “Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market” has been added to from ResearchAndMarkets.com offer.

The discovery and identification of new drug candidates is a time-consuming process, which faces several challenges. One of the main concerns associated with the drug development process is the high attrition rate, which is often related to the trial and error method adopted for lead identification. In this context, only a small percentage of pharmacological leads are ultimately translated into potential candidates for clinical studies. Moreover, among these candidates, nearly 90% are unable to advance further in the development process. This, in turn, results in a significant loss for drug developers, in terms of resources and finances.

Typically, a prescription drug requires at least 10 years to hit the market and an average investment of over $2 billion. Furthermore, 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 a leading tool, which has been shown to have the potential to address a number of existing challenges. As a result, pharmaceutical players 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 used at different stages in the process of drug development. Specifically, AI-based solutions are widely used in combination with deep learning algorithms to produce actionable insights for target identification, hit generation, and lead optimization. These solutions are expected to increase overall R&D productivity and reduce clinical failures of product candidates.

Additionally, estimates suggest that in 2022 the adoption of AI-based solutions for drug discovery is expected to deliver cost savings worth $8.57 billion, with market projections suggesting cost savings. of more than 28 billion USD by 2035. Despite the fact that niche startups are at the forefront of innovation in this field, several large pharmaceutical players are also actively acquiring capabilities for these technologies.

Many technology giants, such as Google, IBM and Microsoft, have either developed their own products or offer solutions through collaborations with other industry players; for example Google’s DeepMind and IBM Watson. Even though only a few of these AI-powered platforms went public, the developers experienced tremendous growth in share value as their respective platform/product candidates progressed through the various stages of development. Considering historical and contemporary scenarios, we believe that the AI-based drug discovery market presents lucrative investment opportunities for both short-term and long-term investors.

Report scope

The “Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market (Emphasis on the Need for AI-Based Drug Discovery, Key Players Market Landscape, Analysis of Product Offerings and Proposals value affiliates, historical funding activity overview, startup health indexing, potential corporate opportunities, financial analysis of key public companies, market forecasts and opportunity analysis, publicly disclosed investor exit information and Key Acquisition Targets)” provides in-depth insights into the AI-Driven 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 opportunities in this area are likely to evolve, in terms of future business success, over the next decade. The information in this report was presented over several deliverables, including MS Excel sheets (some of which include interactive elements) and an MS PowerPoint presentation, which summarizes the main findings of the project and insights gained from the curated data.

The report contains the following details:

  • A qualitative and quantitative perspective (whenever information was available) on the current need for AI in the field of drug discovery. It presents details on the main applications of AI in drug discovery, as well as information on the advantages of using these methodologies over conventional discovery approaches. Furthermore, it highlights various challenges encountered during the different stages of drug discovery and the opinions of representatives of major stakeholder companies involved in this field.

  • A detailed analysis of companies focused on AI-based drug discovery that were created after 2005, with entries on observed trends related to basic input parameters, such as year of creation, location head office, size of business and type of business.

  • A quantitative perspective on the relative health (based on basic company details, product details, funding activity, and estimated revenue and profit) of the companies that have been profiled in detail in this report. This analysis is based on a proprietary scoring criterion, which has been informed by secondary research.

  • An assessment of the various affiliate products and services, offered by the companies mentioned above, including an analysis based on the 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 for end users, value for developers and others.

  • A company competitiveness analysis, which provides a quantitative basis for comparing the strengths/contributions of various industry players who are involved in providing AI-based services and platforms for drug discovery, captured in this report. It should be mentioned that this analysis is based on the information generated by the relative health indexing and the aforementioned value proposition analyses.

  • A detailed analysis of funding and investment activity that has taken place in this area since 2011. It also includes trends by funding category, outlining the relative maturity (in terms of number of funding instances and capital Total Raised) of the key companies discussed in the report. Additionally, it presents a list of top AI investors in the drug discovery market, based on their participation in funding activities in this industry segment.

  • An in-depth examination of the entire AI-Driven Drug Discovery Market from a financial perspective, including detailed fundamental analysis (balance sheet overviews and key financial ratios) and technical analysis (change overviews historical and recent stock prices and analysis using popular stock performance indicators) financial data of listed companies in the market landscape data set.

  • A business risk analysis, focusing on some of the major categories of risk that are usually discussed in the industry, i.e. operations risk, general business risk, financial risk, associated risk products/technologies and social, economic, environmental and social risks. political risks.

  • Case studies of instances where investors have exited various AI-based drug discovery-related ventures, offering insights into the returns on investment made (subject to data availability). Leveraging the aforementioned details, the report offers an informed opinion on the future outlook for investors in the AI-based drug discovery market.

  • An analysis of key acquisition targets, based on the information generated during 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.

Main topics covered:

Excel deliverables

1. DATA SET ON KEY PLAYERS AND PRODUCTS

2. FUNDING AND INVESTMENT ANALYSIS

3. POTENTIAL INVESTMENT OPPORTUNITIES

4. FUNDAMENTAL AND TECHNICAL FINANCIAL ANALYSIS

5. BUSINESS RISK ANALYSIS

6. MARKET FORECASTS AND OPPORTUNITY ANALYSIS

7. RETURNS ON INVESTMENT

PowerPoint deliverables

1. Background

2. Project approach

3. Project objectives

4. Executive Summary

Section I: Need for an AI-based drug discovery market and market landscape

5. AI-BASED DRUG DISCOVERY MARKET

6. Market Landscape

7. Product landscape and enterprise health indexing

8. Value Proposition Analysis

9. Business Competitiveness Analysis

Section II Investment Analysis

10. Funding and investment analysis

Section III Financial Analysis and Business Risk Assessment

11. Financial analysis of public enterprises

12. Business Risk Analysis

Section IV Market Forecast and Opportunity Analysis

13. Market Forecast and Opportunity Analysis

Section V Investment Returns Analysis, Key Acquisition Objectives and Promising Investment Opportunities

14. Analysis of returns on investment

15. Key acquisition targets

16. Analysis of promising investments

17. Conclusion

18. Appendices

Selection of companies cited

For more information about this report visit https://www.researchandmarkets.com/r/sy5vpb

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