Artificial intelligence (AI) drug discovery is the process of applying advanced algorithms and machine learning to large biological and chemical datasets in order to find potential drug candidates. AI’s ability to rapidly and precisely analyze highly complex scientific data potentially results in faster discovery of drug candidates, reducing the time taken by a factor of 15 and offering a greater chance of success. Many large pharmaceutical companies are collaborating with AI companies to offer drug discovery services to speed up the delivery of medicines to the market.
Although there is no fully AI-developed drug yet on the market, a few drug candidates have already advanced into clinical trials. A drug discovered by the AI startup Exscientia’s drug discovery platform reached clinical trial stage within 12 months instead of the five years expected for similar development in the traditional drug development process. AI models are also accelerating the delivery of repurposed Covid-19 drugs (meaning reusing existing drugs for new treatments) with a few currently being tested at the clinical trial stage.
BenevolentAI ’s repurposed drug for Covid-19 has been granted the FDA’s Emergency Use Authorization.
Exscientia has been screening more than 15,000 drugs (comprising some approved and others clinically ready) to assess their effectiveness against Covid-19.
Insilico Medicine launched COVIDomic, a system for Covid-19 basic and clinical research.
AbCellera has discovered antibody treatment for Covid-19, Bamlanivimab, using its AI-powered discovery platform, which received the FDA’s Emergency Use Authorization.
Google’s DeepMind used its AlphaFold AI platform to publish predictions of protein structures associated with the virus.
The AI drug discovery industry consists of:
AI SaaS companies: A large number of startups in the AI drug discovery space operate as AI SaaS (Software-as-a-Service) companies providing their ML platform to other pharma companies. These pure play startups often specialize in a specific stage of the drug discovery process, and have not expanded into latter stages of drug development.
AI drug discovery and development companies: Majority of the well funded startups in the AI drug discovery space operate as drug discovery and development companies. These are biotechnology companies that have integrated AI as a core component of their in-house drug development process. They typically have a few in-house drug development programs and also collaborate with pharma companies to develop certain drugs.
Pharma incumbents: Large pharma companies operate in this space through collaborations with AI startups or tech companies or by developing in-house AI capabilities.
Tech incumbents: Tech companies which are known for their strong AI capabilities have also entered the industry and offer machine learning tools to support drug discovery. Tech incumbents simply offer ML tools and are not specialized in any particular segment. Hence, they are not included in the market map.
Over 90% of the startups are at the seed/early stages. Most AI SaaS platform operators are yet to achieve stable revenue despite collaborations with pharma companies. AI biotechnology startups have not commercialized their drug with only a few reaching the clinical trial stage.
Both artificial intelligence (AI) Software-as-a-Service (SaaS) companies and AI-driven biotechnology startups often collaborate with big pharma companies to leverage from the benefits of synergy or due to the lack of funding/expertise to develop drugs in-house. Given that most big pharma companies have also integrated AI into their drug development process in some form, disruptors’ value proposition comes from their specialization and unique technologies.
Recursion Pharmaceuticals is a biotechnology company that combines experimental biology and artificial intelligence (AI) to identify treatments for rare diseases, aging, inflammation, infectious diseases, and immunology. The company's approach is based on experimenting by making healthy cells sick, scanning images, and experimenting with its AI platform to understand how they differ from healthy cells. Its lab can perform one million such experiments a week.
Recursion operates as a full-stack player engaged in drug discovery as well as development. It has four drugs targeted at rare diseases in the clinical trial stage. It is also partnering with large pharma companies in more competitive areas. Bayer became a strategic investor and entered into a partnership to jointly develop at least 10 new treatment programs. Both companies will jointly own the projects, and Recursion will generate milestone payments of USD 100 million per program and royalties on future sales. Bayer’s partnership is the company’s third big pharma collaboration, following partnerships with Sanofi and Takeda.
In June 2021, the company entered into a collaboration with the Montreal-based Mila - Quebec Artificial Intelligence (AI) Institute, to leverage its network of expert scientists and professionals to accelerate the company’s machine learning capabilities. The company also announced it will launch its first major multidisciplinary expansion, in Toronto, to further enhance tech-enabled drug discovery, by the end of 2021.
The company went public on the Nasdaq in April 2021, raising USD 508.1 million.
AI Drug Discovery & Development:
AI SaaS | Data Aggregation and Research:
AI SaaS | Biomarker Development:
Incumbents consist of large pharma companies which have integrated artificial intelligence (AI) into their drug development process in some form or entered into collaborations with AI startups and technology companies that offer AI-driven solutions for drug discovery.
Leading big pharma companies have developed in-house AI capabilities, but have not disclosed the extent to which AI is being used in the drug development process. All leading big pharma companies are actively engaged in collaborations with AI startups or tech companies. This is because building in-house capabilities to match the specialization offered by startups is challenging, especially with the scarcity of personnel skilled in both AI and biology.
No investor data is available