Molecule Generation

Drug Candidate Generation uses AI to produce novel chemical compounds with targeted biological properties, enabling acceleration of drug discovery, by generating diverse molecule candidates and evaluating them against biological activity criteria, across pharmaceutical research workflows.

Drug discovery depends on screening large libraries of existing or synthesised molecules to find candidates with the desired biological activity, a process that is slow, resource-intensive, and yields few viable leads relative to the number of compounds tested.

The AI generates diverse novel molecular structures optimised for defined biological properties, producing a set of candidate compounds ranked by predicted activity and synthesisability for experimental validation.

Accelerated drug discovery process with higher quality molecule candidates

• •A curated molecular database with known biological activity labels is accessible for training
• •Computational chemistry or molecular simulation infrastructure is available for candidate validation