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.

在计算药物发现领域，在维持合成可及性与类药性的同时实现最优靶点活性，是一项核心挑战。现有从头生成模型往往会产出化学上无效或合成上难以实现的结构，且难以同时优化多个目标。在此，我们提出ALCHIMIA——一种可解释的混合框架，结合了强化学习（Reinforcement Learning, RL）与遗传算法（Genetic Algorithm, GA），其构建基于33种受药物化学启发的分子转化词表。强化学习模块训练策略网络，优先选择可提升合成可及性（Synthetic Accessibility, SA）与类药性定量评估（Quantitative Estimate of Drug-likeness, QED）分数的转化序列，并将这些约束直接嵌入分子生成过程。遗传算法模块将习得的策略作为变异算子，应用于基于分子对接引导的种群优化过程中，既能探索多样的化学谱系，又能向高亲和力配体收敛。我们将ALCHIMIA应用于两个药理学相关靶点：人源大麻素受体2（human Cannabinoid Receptor 2, CB2R）与人源σ非阿片类细胞内受体1（human Sigma nonopioid intracellular Receptor 1, S1R）。我们考量了三种不同场景：(i) 无约束命中物识别；(ii) 骨架约束的先导化合物优化；(iii) 双重调节剂设计。该框架生成的化学有效分子，其QED与SA分数可与随机基线及现有从头设计方法的结果相媲美，甚至更优。通过将典型药物化学操作编码为可学习的转化形式，并将多目标优化与基于遗传算法的多样性维持相结合，ALCHIMIA以GitHub仓库（https://github.com/alberdom88/ALCHIMIA）的形式免费开放，为分子从头设计提供了一套实用、可解释且可扩展的框架。