Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization

TL;DR

Mol-LLM is a novel multimodal molecular language model that explicitly incorporates molecular graph structures, significantly improving performance across diverse molecular tasks compared to previous models.

Contribution

The paper introduces Mol-LLM, the first multimodal generalist molecular LLM that effectively utilizes molecular graph information through novel training strategies and architecture enhancements.

Findings

Achieves state-of-the-art results on molecular benchmarks.

Outperforms prior models on out-of-distribution datasets.

Effectively leverages molecular graph structures in LLMs.

Abstract

Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Large-scale molecular instruction-tuning datasets have enabled sequence-only (e.g., SMILES or SELFIES) generalist molecular LLMs, and researchers are now exploring multimodal approaches that incorporate molecular structural information for further gains. However, a genuinely multimodal, generalist LLM that covers a broad spectrum of molecular tasks has yet to be fully investigated. We observe that naive next token prediction training ignores graph-structural information, limiting an LLM's ability to exploit molecular graphs. To address this, we propose (i) Molecular structure Preference Optimization (MolPO), which facilitates graph usage by optimizing preferences between pairs of correct and perturbed molecular structures, and (ii) an advanced graph encoder with a tailored pre-training strategy to improve the effect of graph utilization by MolPO. Building on these contributions, we introduce Mol-LLM, the first multimodal generalist model that (a) handles a broad spectrum of molecular tasks among molecular LLMs, (b) explicitly leverages molecular-structure information, and (c) takes advantage of extensive instruction tuning. Mol-LLM attains state-of-the-art or comparable results across the most comprehensive molecular-LLM benchmark-even on out-of-distribution datasets for reaction and property prediction, where it surpasses prior generalist molecular LLMs by a large margin.