# GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization

arXiv: https://arxiv.org/abs/2502.13398 · HTML: https://mlsanity.com/paper/2502.13398/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization](https://mlsanity.com/paper/2206.12411/) (2022)
- [LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset](https://mlsanity.com/paper/2402.09391/) (2024)
- [MARS: Markov Molecular Sampling for Multi-objective Drug Discovery](https://mlsanity.com/paper/2103.10432/) (2021)
- [Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation](https://mlsanity.com/paper/2110.01219/) (2021)
- [DrugAssist: A Large Language Model for Molecule Optimization](https://mlsanity.com/paper/2401.10334/) (2023)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [A Deep Generative Model for Molecule Optimization via One Fragment Modification](https://mlsanity.com/paper/2012.04231/) (2020)
- [Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation](https://mlsanity.com/paper/2412.14642/) (2024)
- [Molecule Generation with Fragment Retrieval Augmentation](https://mlsanity.com/paper/2411.12078/) (2024)

## Built on by (most cited first, 17 of 17)

- [A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization](https://mlsanity.com/paper/2505.16094/) (2025)
- [POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization](https://mlsanity.com/paper/2509.21737/) (2025)
- [Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning](https://mlsanity.com/paper/2603.05900/) (2026)
- [SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?](https://mlsanity.com/paper/2605.21740/) (2026)
- [BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation](https://mlsanity.com/paper/2512.04629/) (2025)
- [Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization](https://mlsanity.com/paper/2505.23987/) (2025)
- [AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models](https://mlsanity.com/paper/2508.04748/) (2025)
- [Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs](https://mlsanity.com/paper/2510.01527/) (2025)
- [MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization](https://mlsanity.com/paper/2512.20135/) (2025)
- [MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning](https://mlsanity.com/paper/2505.20131/) (2025)
- [SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing](https://mlsanity.com/paper/2605.10831/) (2026)
- [MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery](https://mlsanity.com/paper/2603.03517/) (2026)
- [MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models](https://mlsanity.com/paper/2603.18256/) (2026)
- [Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models](https://mlsanity.com/paper/2604.12350/) (2026)
- [HALO: Interactive Co-abductive Reasoning in Scientific Hypothesis Generation](https://mlsanity.com/paper/2607.18564/) (2026)
- [Molecular LLM Agents: From Architectural Design to Scientific Autonomy](https://mlsanity.com/paper/2608.23104/) (2026)
- [M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization](https://mlsanity.com/paper/2609.34491/) (2026)

## Abstract (arXiv)

Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability and generalizability to novel optimization tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable out-of-domain generalizability to novel tasks. To demonstrate LLMs' potential for molecule optimization, we introduce MuMOInstruct, the first high-quality instruction-tuning dataset specifically focused on complex multi-property molecule optimization tasks. Leveraging MuMOInstruct, we develop GeLLMOs, a series of instruction-tuned LLMs for molecule optimization. Extensive evaluations across 5 in-domain and 5 out-of-domain tasks demonstrate that GeLLMOs consistently outperform state-of-the-art baselines. GeLLMOs also exhibit outstanding zero-shot generalization to unseen tasks, significantly outperforming powerful closed-source LLMs. Such strong generalizability demonstrates the tremendous potential of GeLLMOs as foundational models for molecule optimization, thereby tackling novel optimization tasks without resource-intensive retraining. MuMOInstruct, models, and code are accessible through https://github.com/ninglab/GeLLMO.