M3-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery

作     者：Guo, Siyuan Wang, Lexuan Jin, Chang Wang, Jinxian Peng, Han Shi, Huayang Li, Wengen Guan, Jihong Zhou, Shuigeng 

作者机构：Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai201804 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 2005 Songhu Road Shanghai200438 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Web crawler 

摘      要：This paper introduces M3-20M, a large-scale Multi-Modal Molecule dataset that contains over 20 million molecules, with the data mainly being integrated from existing databases and partially generated by large language models. Designed to support AI-driven drug design and discovery, M3-20M is 71 times more in the number of molecules than the largest existing dataset, providing an unprecedented scale that can highly benefit the training or fine-tuning of models, including large language models for drug design and discovery tasks. This dataset integrates one-dimensional SMILES, two-dimensional molecular graphs, three-dimensional molecular structures, physicochemical properties, and textual descriptions collected through web crawling and generated using GPT-3.5, offering a comprehensive view of each molecule. To demonstrate the power of M3-20M in drug design and discovery, we conduct extensive experiments on two key tasks: molecule generation and molecular property prediction, using large language models including GLM4, GPT-3.5, GPT-4, and Llama3-8b. Our experimental results show that M3-20M can significantly boost model performance in both tasks. Specifically, it enables the models to generate more diverse and valid molecular structures and achieve higher property prediction accuracy than existing single-modal datasets, which validates the value and potential of M3-20M in supporting AI-driven drug design and discovery. The dataset is available at https://***/bz99bz/M-3. Copyright © 2024, The Authors. All rights reserved.