Selecting which targets to work on within a chosen therapeutic area is one of the biggest challenges of drug discovery. A steady progression of downstream effort flows from that single decision.

At Exscientia, we have taken the view that no single laboratory can offer the solution to target selection. Instead, we have concentrated on novel approaches to mine the public corpus of genetic data and biology literature to determine when there is sufficient information to initiate work on a target or interest.

Our systems apply deep learning to comprehensive knowledge graphs allowing us to understand and exploit global research trajectories and emerging hotspots of attention.

Most diseases are highly networked, meaning therapies often need to hit multiple nodes to have a sustainable effect. We have extended our AI platform so it can flexibly design and evolve single molecules that work against more than one target.

The most accessible implementation is for dual targets and for these projects we refer to our dual-targeted compounds as bispecific small molecules (not to be confused with bispecific antibodies).

A range of innovative projects become possible with this approach, ranging from selectively hitting two members of the same family to identifying unrelated targets with complementary binding sites. Both approaches have both been exemplified with our partners Dainippon Sumitomo and Sanofi.