Mol-LLM: Generalist Molecular LLM with Improved Graph Utilization

• URL: http://arxiv.org/abs/2502.02810v1
• Date: Wed, 05 Feb 2025 01:14:12 GMT
• Title: Mol-LLM: Generalist Molecular LLM with Improved Graph Utilization
• Authors: Chanhui Lee, Yuheon Song, YongJun Jeong, Hanbum Ko, Rodrigo Hormazabal, Sehui Han, Kyunghoon Bae, Sungbin Lim, Sungwoong Kim,
• Abstract summary: Large Language Models (LLMs) have motivated the development of general LLMs for molecular tasks. LLMs trained with naive next-token prediction training assign similar likelihood scores to both original and corrupted molecules. We introduce a novel multi-modal training method based on a thorough multi-modal instruction tuning and a molecular structure preference optimization.
• Score: 8.846705148987652
• License:
• Abstract: Recent advances in Large Language Models (LLMs) have motivated the development of general LLMs for molecular tasks. While several studies have demonstrated that fine-tuned LLMs can achieve impressive benchmark performances, they are far from genuine generalist molecular LLMs due to a lack of fundamental understanding of molecular structure. Specifically, when given molecular task instructions, LLMs trained with naive next-token prediction training assign similar likelihood scores to both original and negatively corrupted molecules, revealing their lack of molecular structure understanding that is crucial for reliable and general molecular LLMs. To overcome this limitation and obtain a true generalist molecular LLM, we introduce a novel multi-modal training method based on a thorough multi-modal instruction tuning as well as a molecular structure preference optimization between chosen and rejected graphs. On various molecular benchmarks, the proposed generalist molecular LLM, called Mol-LLM, achieves state-of-the-art performances among generalist LLMs on most tasks, at the same time, surpassing or comparable to state-of-the-art specialist LLMs. Moreover, Mol-LLM also shows superior generalization performances in reaction prediction tasks, demonstrating the effect of the molecular structure understanding for generalization perspective.

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