AI Case Study

GlaxoSmithKline (GSK) plans to accelerate drug discovery as well as new applications for existing drugs using machine learning

GSK in partnership with Exscientia is exploring ways to accelerate drug discovery by identifying selective small molecules for 10 disease-related targets across multiple therapeutic areas. They plan to reduce the number of compounds required for synthesis in response to early stage research. They will then use machine learning to design molecules that fulfill lead and candidate requirements.

Industry

Healthcare

Pharmaceuticals And Biotech

Project Overview

"Novel compounds prioritised for synthesis by Exscientia's AI systems simultaneously balance potency, selectivity and pharmacokinetic criteria in order to deliver successful experimental outcomes. By applying a rapid design-make-test cycle, the Exscientia AI system actively learns from the preceding experimental results and rapidly evolves compounds towards the desired candidate criteria.

During this collaboration, Exscientia will apply its AI enabled platform and combine this with the expertise of GSK, in order to discover novel and selective small molecules for up to 10 disease-related targets, nominated by GSK across multiple therapeutic areas.

Exscientia will receive research payments from GSK to undertake new discovery programmes with nominated targets with the goal of delivering pre-clinical candidates. In addition to research funding, Exscientia is eligible to receive near-term lead and pre-clinical candidate milestones if all objectives are achieved."

Reported Results

Expected to significantly reduce lead time in research

Technology

Function

R And D

Product Development

Background

GSK following the trend of major pharmaceuticals is investing in AI to automate drug design in an attempt to reduce discovery and trial times.

Benefits

Data