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PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints

PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties such as drug‑likeness or binding affinity while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. The method addresses a reward‑hacking failure mode by treating each input molecule as the start of a forward‑synthesis trajectory, applying learned reaction templates with in‑stock building blocks, and producing diverse, high‑quality outputs with explicit synthesis routes.

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arXiv AI
Aug 20

PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints

PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. Experiments demonstrate that PGFS++ enhances target properties and preserves high output diversity, overcoming the reward‑hacking failure mode seen in earlier versions.

By Boqiao Zhang, Godbless James, Sai Krishna Gottipati, Andrew Fitzgibbon
ragreinforcement-learning
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arXiv Machine Learning
Jun 10

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors

arXiv:2602. 04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice.

By Hyeonah Kim, Minsu Kim, Celine Roget, Dionessa Biton, Louis Vaillancourt, Yves V. Brun, Yoshua Bengio, Alex Hernandez-Garcia
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arXiv Machine Learning
Aug 27

A General-Purpose Framework for Chemical Reaction Representation with Atomic Correspondence and Flexible Condition Adaptation

The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.

By Kaipeng Zeng, Xianbin Liu, Yu Zhang, Xiaokang Yang, Yaohui Jin, Yanyan Xu
benchmarks
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arXiv Machine Learning
Aug 26

Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs

The paper introduces Round-Trip Reinforcement Learning (RTRL), a framework that trains chemical language models to improve round‑trip consistency by rewarding successful forward and reverse transformations. By iteratively training forward and reverse mappings, RTRL leverages abundant unlabeled chemical data to enhance both consistency and overall performance across supervised, self‑supervised, and synthetic data regimes. Experiments show that RTRL outperforms strong baselines, demonstrating that round‑trip consistency can be treated as a trainable objective for more robust foundation models.

By Lecheng Kong, Xiyuan Wang, Yixin Chen, Muhan Zhang
llmsreinforcement-learningbenchmarks
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