A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations

Achieving optimal target activity while maintaining synthetic
accessibility
and drug-likeness represents a major challenge in computational drug
discovery. Existing de novo generative models often
yield chemically invalid or synthetically intractable structures and
struggle to optimize multiple objectives simultaneously. Here, we
introduce ALCHIMIA, an interpretable hybrid framework combining reinforcement
learning (RL) and a genetic algorithm (GA), built based on a vocabulary
of 33 medicinal chemistry-inspired molecular transformations. The
RL component trains a policy network to prioritize transformation
sequences that improve synthetic accessibility (SA) and the quantitative
estimate of drug-likeness (QED) scores, embedding these constraints
directly into molecular generation. The GA component applies the learned
policy as a mutational operator within population-based optimization
guided by molecular docking, enabling the exploration of diverse chemical
lineages while converging toward high-affinity ligands. ALCHIMIA was
applied to two different pharmacologically relevant targets: human
Cannabinoid Receptor 2 (CB2R) and human Sigma nonopioid intracellular
Receptor 1 (S1R). We considered three different scenarios: (i) unconstrained
hit identification; (ii) scaffold-constrained lead optimization; and
(iii) design of dual modulators. The framework generated chemically
valid molecules with QED and SA scores comparable to or better than
those obtained with random baselines and selected de novo design methods. By codifying typical medicinal chemistry actions
as learnable transformations and coupling multiobjective optimization
with GA-based diversity maintenance, ALCHIMIA, freely available as
a GitHub repository (https://github.com/alberdom88/ALCHIMIA), provides
a practical, interpretable, and scalable framework for molecular de novo design.