A Landscape of Artificial Intelligence (AI) In Drug Discovery
This market research report aims at providing a “bird’s view” on the emerging ecosystem of AI-based technology companies (primarily, startups) focused on drug discovery and development. 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 based solely on publicly available data from the Internet. Please, send any suggestions and comments about its contents to email@example.com.
The below diagram shows a trend in the number of AI-driven startups tackling drug discovery, with new ones emerging every year, and a distribution of the number of such companies year by year. As one can see, there has been a steep growth in the number of emerging players since 2015, which is now slowing down somewhat. We might expect a decline in the absolute number of players in 2020 due to likely M&A deals with the participation of big pharma and large technology corporations, and disappearance of some of the smaller companies from the arena.
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 below chart shows how the AI-driven startups included in this study are grouped by their relative expertise in chemistry vs. biology. Understandably, most drug discovery research organisations take into account both aspect, but here we discuss the core competency.
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).
Refer to “The AI Map of Drug Discovery” to review a catalog of AI-startups offering their expertise for pharma R&D industry.
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