Unlocking the potential of AI in drug discovery
This report explores the application of artificial intelligence (AI) in drug discovery. Through a review of literature, patents, funding sources, a survey and interviews of practitioners, the report describes the current status, barriers and future opportunities for AI in drug discovery.
The aim is to provide a fact base for those looking to understand this rapidly evolving field. It is also intended for funders that are considering how to engage on this critical topic.
- An overview of major applications and use cases of AI in drug discovery.
- An assessment of the maturity of these use cases for different modalities (for example, small molecule drugs, biologics, vaccines), different stages of the drug discovery process and different therapeutic areas.
- A review of the current adoption of AI in drug discovery, as well as barriers limiting its use.
- Suggestions on how to overcome barriers to unlock the potential of AI in drug discovery.
Who this is for
- Researchers in academia and in the pharmaceutical and biotechnology industry
The field of AI in drug discovery is maturing rapidly, though unevenly
- There’s been rapid growth in research, patents and funding seen in the last five years, especially in the application of AI in understanding diseases and in small molecule discovery.
- Efforts are concentrated in specific therapy areas like oncology, Covid-19 and neurology, and in specific settings, especially industrial research in high-income countries and China.
Adoption of AI tools varies significantly
- Adoption of AI tools in drug discovery is growing, but it’s still limited in many settings.
- The industry adoption of AI approaches, on average, is higher than academia. Adoption is led by ‘AI-first’ biotech companies that have research and development workflows built around AI tools.
- Despite varying levels of adoption, there is broad consensus on the future potential of this technology, with more than 80% of current AI users (and 70% of current non-users) stating that they expect AI to drive significant impact in drug discovery over the next five years.
Early proof points are emerging, but a key test will come in the clinic
- There are emerging proofs of value in three areas, namely:
- time and cost savings
- increased probability of success
- novelty of both the molecular target and optimised therapeutic agent.
- When taken together, modelling suggests that time and cost savings could be approximately 25 to 50%, which could materially impact the economics of drug discovery.
- While there are examples of early clinical successes, a more thorough analysis in the coming years will be needed to truly evaluate the impact of AI on drug discovery.
Barriers must be addressed to unlock the full potential of AI
- Several barriers currently limit adoption, including:
- lack of trust in the value of AI
- limited access, low maturity and lack of standardisation of data, tools and capabilities.
- Market failure may limit the applicability of key tools in less commercially attractive therapeutic areas – with the potential for AI to amplify disparities in health equity.
- Access to interdisciplinary capabilities such as computational chemistry (the use of computers to solve chemical problems) and bioinformatics (the science of using computer science to understand biological data) has emerged as an additional barrier in many settings.
Several initiatives are emerging to tackle barriers. However, on the current trajectory, these initiatives will be insufficient to unlock the potential of AI in drug discovery in ways that can equitably address urgent health needs.
Funders of health research – from basic science to translation and product development – stand to benefit from unlocking the potential of AI in drug discovery and can collectively have enormous influence on how this field evolves.
Funders can take these six key actions:
- Find value from AI today.
- Take no-regret moves to maximise future value.
- Build coalitions to set 'rules of the road'.
- Invest where AI intersects with drug discovery goals.
- Contribute to the public debate.
- Build organisational capabilities to enable delivery.