Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization

• URL: http://arxiv.org/abs/2505.23987v1
• Date: Thu, 29 May 2025 20:29:14 GMT
• Title: Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
• Authors: Vishal Dey, Xiao Hu, Xia Ning,
• Abstract summary: We introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives.<n>We develop GeLLMO-Cs, a series of instruction-tuned LLMs that can perform targeted property-specific optimization.<n>Our experiments show that GeLLMO-Cs consistently outperform strong baselines, achieving up to 126% higher success rate.
• Score: 2.152507712409726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that already meet such criteria. However, existing computational approaches and instruction-tuned LLMs fail to capture such nuanced property-specific objectives, limiting their practical applicability. To address this, we introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives. Leveraging C-MuMOInstruct, we develop GeLLMO-Cs, a series of instruction-tuned LLMs that can perform targeted property-specific optimization. Our experiments across 5 in-distribution and 5 out-of-distribution tasks show that GeLLMO-Cs consistently outperform strong baselines, achieving up to 126% higher success rate. Notably, GeLLMO-Cs exhibit impressive 0-shot generalization to novel optimization tasks and unseen instructions. This offers a step toward a foundational LLM to support realistic, diverse optimizations with property-specific objectives. C-MuMOInstruct and code are accessible through https://github.com/ninglab/GeLLMO-C.

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