Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning

Lead optimization supported by artificial intelligence
(AI)-based
generative models has become increasingly important in drug design.
Success factors are reagent availability, novelty, and the optimization
of multiple properties. Directed fragment-replacement is particularly
attractive, as it mimics medicinal chemistry tactics. Here, we present
variations of fragment-based reinforcement learning using an actor-critic
model. Novel features include freezing fragments and using reagents
as the fragment source. Splitting molecules according to reaction
schemes improves synthesizability, while tuning network output probabilities
allows us to balance novelty versus diversity. Combining fragment-based
optimization with virtual library encodings allows the exploration
of large chemical spaces with synthesizable ideas. Collectively, these
enhancements influence design toward high-quality molecules with favorable
profiles. A validation study using 15 pharmaceutically relevant targets
reveals that novel structures are obtained for most cases, which are
identical or related to independent validation sets for each target.
Hence, these modifications significantly increase the value of fragment-based
reinforcement learning for drug design. The code is available on GitHub:
https://github.com/Sanofi-Public/IDD-papers-fragrl