PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
Research area: Machine Learning Authors: Boqiao Zhang, Godbless James, Sai Krishna Gottipati, Andrew Fitzgibbon Published: 2026-08-19 arXiv: 2608.19121
Summary
Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optimized in an unconstrained chemical space have limited practical value if they cannot be synthesized. Policy Gradient for Forward Synthesis (PGFS) is a synthesis-aware reinforcement learning method for molecular improvement, but its use of reactant embedding prediction makes reactant selection indirect, which, as the authors show, limits learning effectiveness.
The authors first develop PGFS+, in which reaction templates and second reactants are represented by trainable embedding lookup tables. Combined with a more effective scoring function and RL algorithm, PGFS+ significantly improves the desired property. However, it exposes a reward-hacking failure mode: a powerful reactant search can map diverse input molecules to the same high-reward "magnet" molecule, improving reward while collapsing output diversity.
To address this, the authors introduce PGFS++, a synthesis-aware reinforcement learning framework for input-specific molecular improvement. Given an input molecule, PGFS++ treats it as the starting point of a forward synthesis trajectory, applies learned reaction templates with compatible in-stock building blocks, and produces molecules with improved target properties, explicit synthetic routes, and structural similarity to the input. Experiments on molecular improvement tasks show that PGFS++ improves target properties while maintaining high output diversity.
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