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A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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 a manually curated database of companies. This report is based solely on publicly available data from the Internet. Please, send any suggestions and comments about its contents to

The below diagram shows how many of AI-based drug discovery startups emerge each year, and how much VC money they get collectively (only publicly announced rounds included).

Another important indicator of pharma industry’s willingness to adopt AI innovations is the number of collaborations between prominent pharma players and AI-technology vendors (startups). As one can see, the number of collaborations has been growing so far to reach the maximum in 2018.

Number of New AI-startups, Amounts of VC funding and Deals With Pharma over Years

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.

Chemistry vs Biology Focus In AI-startups

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.

AI-startups and VC-funding Distribution By Geography

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 by Their Product Focus
AI-startups by Drug Discovery Approach

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.

AI-startups by Therapeutic Focus

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.

AI-startups by Pharma Research Use Case

Finally, the companies were clustered conditionally by the primary type of data they use for training AI-models (highly overlapping in many cases).

AI-startups by Type of Data They Use For Drug Discovery
AI-startups by Business Model

Refer to “The AI Map of Drug Discovery” to review a catalog of AI-startups offering their expertise for pharma R&D industry.

This report is presented in an aggregate format to have a general idea about the industry trend. To get detailed information about each company or any custom analytics, please, contact us at

Please, feel free to suggest your companies to be included in “The AI Map of Drug Discovery”, or updates to be made on the existing company profiles. Those are to be sent to