Market Research Report
AI-based Drug Discovery Market: Focus on Deep Learning and Machine Learning, 2020-2030
|AI-based Drug Discovery Market: Focus on Deep Learning and Machine Learning, 2020-2030|
Published: January 31, 2021
Content info: 294 Pages
Delivery time: 1-2 business days
The drug discovery process, which includes the identification of a relevant biological target and a corresponding pharmacological lead, is crucial to the clinical success of a drug candidate. Considering the growing complexity of modern pharmacology, the discovery of viable therapeutic candidates is very demanding, both in terms of capital investment and time. In fact, according to a study conducted by Tufts Center for the Study of Drug Development, it was estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, while traversing from the bench to the market. Around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone. Moreover, it is well-known that only a small proportion of pharmacological leads identified during the discovery stages are actually translated into viable product candidates for clinical studies. Currently, experts believe that close to 90% of the product candidates fail to make it past the clinical stage of development. This high attrition rate has long been attributed to the legacy drug discovery process, which is more of a trial-and-error paradigm. In attempts to address the concerns associated with rising capital requirements in drug discovery, and prevent late stage failure of drug development programs, stakeholders in the pharmaceutical industry are currently exploring the implementation of Artificial Intelligence (AI) based tools in order to better inform drug development operations using available chemical and biological data.
Over time, AI-based tools have been gradually deployed across various processes, including drug discovery, within the healthcare sector. The predictive power of AI is primarily based on the processing and analysis of large volumes of clinical / medical data, which is now being leveraged to better inform modern drug discovery efforts. In this context, deep learning algorithms have demonstrated the ability to cross-reference published scientific literature (structured data) with electronic health records available in public medical data banks and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization. In other words, the use of AI-enabled technologies in drug discovery operations is likely to not only improve overall R&D productivity, but also reduce clinical failure with accurate predictions of a drug candidate's safety and efficacy during the early stages of product development. Although the adoption of such advanced tools is still limited in mainstream drug development programs, it is worth mentioning that, in the last five years alone, close to USD 5 billion was invested into companies that are developing AI-based solution for drug discovery applications. Interestingly, ~50% of the aforementioned amount was invested in the last two years. Therefore, we are led to believe that the opportunity for stakeholders in this niche, but upcoming industry is likely to grow at a commendable pace in the foreseen future.
The "AI-based Drug Discovery Market: Focus on Machine Learning and Deep Learning, 2020-2030" report features an extensive study of the current market landscape and future potential of the players engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain. Amongst other elements, the report features:
One of the key objectives of this report was to estimate the existing market size and the future growth potential within the AI-based drug discovery market. We have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] geographical regions (North America (the US and Canada), Europe (the UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies, and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.
The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain. The report features detailed transcripts of interviews held with the following individuals (in alphabetical order) :
All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.
Chapter 2 is an executive summary of the key insights captured in our research. It offers a high-level view on the current state of the AI-based drug discovery market and its likely evolution in the short-mid term and long term.
Chapter 3 is an introductory chapter that presents details on the digital revolution in the healthcare industry. It elaborates on the applications of artificial intelligence and its subsets, including machine learning (supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing). The chapter also provides an overview of the importance of data science. It further emphasizes on the applications of AI in the healthcare sector, along with detailed information on areas, including drug discovery, drug manufacturing, drug marketing, diagnosis and treatment, and clinical trials. Additionally, it features detailed information on the different steps involved in the overall drug discovery process. Further, it highlights the advantages and challenges related to the use of AI in drug discovery.
Chapter 4 provides an assessment of the current market landscape of companies that claim to offer AI-based services, platforms and tools for drug discovery. It includes information on year of establishment, company size (in terms of number of employees), location of headquarters, number of AI-based platforms / tools available, type of AI technology used, drug discovery steps for which the company has expertise involving the use of AI (target identification / validation, lead identification / optimization and ADMET studies), type of drug molecule handled (small molecules, biologics and both), drug development initiatives undertaken by the firm and target therapeutic area.It also provides information on the contemporary trends, which have been presented using three schematic representations, including [A] a logo landscape highlighting the distribution of companies based on expertise across drug discovery steps and company size (in terms of number of employees), [B] a world map representation highlighting the distribution of companies based on availability of number of AI-based platforms / tools, and [C] an insightful grid analysis presenting the distribution of companies based on the type of drug molecules handled, expertise across drug discovery steps and geographical presence.
Chapter 5 provides elaborate profiles of key players that are engaged in the AI-based drug discovery domain. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details related to their respective portfolio of platforms / tools, recent developments and an informed future outlook.
Chapter 6 features brief details related to initiatives undertaken by technology giants in AI-based healthcare sector. The chapter includes information about companies, such as Amazon Web Services, Alibaba Cloud, Google, IBM, Intel, Microsoft and Siemens.
Chapter 7 offers detailed analysis of the partnerships that have been inked by stakeholders engaged in the AI-based drug discovery domain, during the period 2009-2020, including research agreements, research and development agreements, technology access / utilization agreements, technology integration agreements, licensing agreements, acquisitions and other relevant types of deals.
Chapter 8 contains comprehensive analysis of the investments made, including award / grant, seed financing, venture capital financing, debt financing and others, in companies that are involved in AI-based drug discovery.
Chapter 9 provides an elaborate valuation analysis of companies that are involved in the AI-based drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
Chapter 10 includes 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.
Chapter 11 presents a comprehensive market forecast analysis highlighting the future potential of the AI-based drug discovery market till 2030. It features the likely distribution of the market based on [A] geographical regions (North America (US and Canada), Europe (UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.
Chapter 12 is a summary of the overall report. It includes key takeaways related to research and analysis from the report in an infographic format.
Chapter 13 is a collection of interview transcripts of discussions held with key stakeholders in this industry. In this chapter, we have presented the details of our conversations held with Bo Ram Beck (Head Researcher, DEARGEN), Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals) and Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse).
Chapter 14 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.
Chapter 15 is an appendix, which provides the list of companies and organizations mentioned in the report.