The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D
This market research report aims at providing a “bird’s eye view” on the emerging ecosystem of AI-based technology companies (primarily, startups) focused on drug discovery and development, clinical research, post-market analytics. All the graphs and diagrams in this report below are dynamically generated and updated based on data from our semi-automatically curated database of industry information. This report is primarily based on publicly available data, but it also includes exclusive insights from direct interviews with company representatives. Please, send any suggestions and comments about its contents to firstname.lastname@example.org.
The below diagram shows a trend in the number of AI-driven startups tackling drug discovery, with new ones emerging every year, and the distribution of the number of such companies year by year. We expect a wave of IPOs within the next 2-3 years, via traditional routes, or via Special Purpose Acquisition Companies (SPACs) for the leading startups from this report. In 2020-2021 several AI-driven companies already have gone public
The next diagram reveals how much money venture capitalists invest in the “AI for Drug Discovery” startups from the diagram above. We expect the increase in the overall volume of funding due to an increasing number of larger B, C and D rounds for some of the “older” startups.
The below chart is revealing a dynamics in the overall industry deal-making, where large pharmaceutical companies outsource AI-expertise from the small startups via research deals and milestone-based partnerships.
The AI-driven drug discovery ecosystem is primarily focused in the US according to the diagram below. However, this perceived geographical disproportion is largely due to the fact, that many companies, for example, Chinese startups, are actually incorporated in the US. So here we assign geography formally, by the place of incorporation.
The next two diagrams provide a general idea of the research approach that is typical for AI-driven drug discovery startups and technology vendors.
Interestingly, most AI-innovations are focused around small molecules as starting points for drug discovery, employing target-based drug discovery (TBDD) paradigm. This is, probably, predictable as ligand-target interactions are easier to describe and fit into a typical machine learning/AI-driven modeling process. compared to other approaches, such as phenotypic screening.
AI startups are distributed across various therapeutic areas in a more or less the same way as any other emerging biotech startups — oncology is a major dominant as the most attractive area for drug discovery from many points of view.
To review the catalog of AI-technology vendors in the context of various drug discovery stages, please check “The AI Map of Drug Discovery”.
Next, it is seen that AI-technology vendors are involved in almost every research aspect of modern drug discovery and development process — from data mining and biology research all the way to helping organize, manage and improve clinical trials. While some technology vendors focus on a narrow area, like the de-novo design or ADME predictions, others develop end-to-end AI-driven platforms to deliver “ready-to-trial” drug candidates.
Finally, the companies were clustered conditionally by the primary type of data they use for training AI-models (highly overlapping in many cases).
This free report sample is presented to provide some very general insights about the "artificial intelligence in the pharmaceutical industry" segment. To get access to a comprehensive online report, analytics dashboard, and database of company profiles, classified and tagged across dozens of domain-specific parameters, please consider purchasing our report "A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D".