[2603.24382] MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization

Computer Science > Machine Learning arXiv:2603.24382 (cs) [Submitted on 25 Mar 2026] Title:MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization Authors:Xiangsen Chen, Ruilong Wu, Yanyan Lan, Ting Ma, Yang Liu View a PDF of the paper titled MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization, by Xiangsen Chen and 4 other authors View PDF HTML (experimental) Abstract:Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigger drastic property shifts. Current representation learning, bound by the similarity principle, often fails to capture these structural-activity discontinuities. To address this, we introduce MolEvolve, an evolutionary framework that reformulates molecular discovery as an autonomous, look-ahead planning problem. Unlike traditional methods that depend on human-engineered features or rigid prior knowledge, MolEvolve leverages a Large Language Model (LLM) to actively explore and evolve a library of executable chemical symbolic operations. By utilizing the LLM to cold start and an Monte Carlo Tree Search (MCTS) engine for test-time planning with external tools (e.g. RDKit), the system self-discovers optimal trajectories autonomously. This process evolves transparent reasoning chains that translate complex structural transformations into actionable, human-readable chemical insight...