RL-syn-Tyr:Discovery of Tyrosinase Inhibitors from AI De Novo Molecular Generation to Dual-Path Lead Optimization

This study proposes an innovative framework that integrates de novo molecular design with synthetic feasibility evaluation, leveraging RL-driven forward synthesis planning to significantly improve both the generation efficiency and synthetic success rate of target molecules. Initially, 200 high-affinity fragments were selected from a building block database based on molecular docking results to form the starting pool. In subsequent iterations, a diversity-constrained mechanism was introduced-fragments were selected based on structural fingerprint clustering and synthesis complexity weighting to ensure both novelty and coverage. During the molecule generation phase, Monte Carlo Tree Search (MCTS) was used to guide synthetic pathway exploration. The novelty of this framework lies in three key aspects: Bidirectional optimization: Simultaneous optimization of molecular bioactivity and synthetic accessibility; Adaptive exploration: Dynamic reward shaping based on fragment usage frequency to suppress repetitive generation of redundant structures.

Using compound AI10 as the reference molecule, an RL-driven molecular generation strategy to autonomously design target compounds were employed. Specifically, a fragment-based growth and expansion approach was applied, in which the reference molecule was decomposed into multiple query fragments. These fragments were then used to assemble sets of structurally related fragments, from which new molecules were generated by predefined reaction templates. The resulting candidates were evaluated by structural similarity and docking against the reference scaffold, enabling the algorithm to yield compounds with comparable architectures and physicochemical properties. By integrating predicted activity, drug-likeness, synthetic feasibility, and structural similarity to the lead compound AI10, 32 candidate molecules (AI10-a1 to AI10-a32) were prioritized for laboratory synthesis.