# MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization

arXiv: https://arxiv.org/abs/2603.24382 · HTML: https://mlsanity.com/paper/2603.24382/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [3D Infomax improves GNNs for Molecular Property Prediction](https://mlsanity.com/paper/2110.04126/) (2021)
- [GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph](https://mlsanity.com/paper/2105.02605/) (2021)
- [BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine](https://mlsanity.com/paper/2308.09442/) (2023)
- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [Efficient Evolutionary Search Over Chemical Space with Large Language Models](https://mlsanity.com/paper/2406.16976/) (2024)
- [The Origins of Representation Manifolds in Large Language Models](https://mlsanity.com/paper/2505.18235/) (2025)
- [Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations](https://mlsanity.com/paper/2505.21318/) (2025)
- [Interpretable and Explainable Machine Learning for Materials Science and Chemistry](https://mlsanity.com/paper/2111.01037/) (2021)
- [CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning](https://mlsanity.com/paper/2506.07551/) (2025)
- [Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization](https://mlsanity.com/paper/2503.03503/) (2025)
- [Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions](https://mlsanity.com/paper/2409.14572/) (2024)
- [FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability](https://mlsanity.com/paper/2410.12156/) (2024)
- [Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction](https://mlsanity.com/paper/2410.15165/) (2024)
- [MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction](https://mlsanity.com/paper/2506.05427/) (2025)
- [A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation](https://mlsanity.com/paper/2601.04507/) (2026)
- [Automated Molecular Concept Generation and Labeling with Large Language Models](https://mlsanity.com/paper/2406.09612/) (2024)
- [Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling](https://mlsanity.com/paper/2508.00920/) (2025)
- [AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models](https://mlsanity.com/paper/2508.04748/) (2025)

## Built on by (most cited first, 4 of 4)

- [What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search](https://mlsanity.com/paper/2604.19440/) (2026)
- [From Single-Step Edit Response to Multi-Step Molecular Optimization](https://mlsanity.com/paper/2605.10035/) (2026)
- [When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors](https://mlsanity.com/paper/2605.17265/) (2026)
- [Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs](https://mlsanity.com/paper/2607.23286/) (2026)

## Abstract (arXiv)

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 insights. Experimental results demonstrate that MolEvolve's autonomous search not only evolves transparent, human-readable chemical insights, but also outperforms baselines in both property prediction and molecule optimization tasks.