MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization

TL;DR

MolAct introduces a novel agentic reinforcement learning framework for molecular editing and optimization, enabling iterative, tool-guided molecular design with improved validity and property optimization performance.

Contribution

This work formalizes molecular design as an agentic RL problem and develops MolAct, the first framework to integrate reasoning, tool-use, and multi-turn interactions for molecular editing and optimization.

Findings

MolEditAgent-7B achieves 95-100% valid edits, outperforming baselines.

MolOptAgent-7B surpasses top baselines on LogP optimization.

Framework enables reliable, interpretable molecular improvements.

Abstract

Molecular editing and optimization are multi-step problems that require iteratively improving properties while keeping molecules chemically valid and structurally similar. We frame both tasks as sequential, tool-guided decisions and introduce MolAct, an agentic reinforcement learning framework that employs a two-stage training paradigm: first building editing capability, then optimizing properties while reusing the learned editing behaviors. To the best of our knowledge, this is the first work to formalize molecular design as an Agentic Reinforcement Learning problem, where an LLM agent learns to interleave reasoning, tool-use, and molecular optimization. The framework enables agents to interact in multiple turns, invoking chemical tools for validity checking, property assessment, and similarity control, and leverages their feedback to refine subsequent edits. We instantiate the MolAct framework to train two model families: MolEditAgent for molecular editing tasks and MolOptAgent for molecular optimization tasks. In molecular editing, MolEditAgent-7B delivers 100, 95, and 98 valid add, delete, and substitute edits, outperforming strong closed "thinking" baselines such as DeepSeek-R1; MolEditAgent-3B approaches the performance of much larger open "thinking" models like Qwen3-32B-think. In molecular optimization, MolOptAgent-7B (trained on MolEditAgent-7B) surpasses the best closed "thinking" baseline (e.g., Claude 3.7) on LogP and remains competitive on solubility, while maintaining balanced performance across other objectives. These results highlight that treating molecular design as a multi-step, tool-augmented process is key to reliable and interpretable improvements.