3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups
3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups
Tingwei Chen, Jianpeng Chen, Dawei Zhou
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8635-8639.
https://doi.org/10.24963/ijcai.2024/997
Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional groups of a large number of molecules. 3D-FUM integrates 18 state-of-the-art algorithms and 19 evaluation metrics on three molecular learning tasks, including general molecule generation, conditional molecule generation, and property predictions. 3D-FUM, for the first time, take into consideration both 3D geometric information and molecular functional groups, which enables researchers and practitioners to effectively and impartially evaluate newly proposed methods in comparison to existing baselines across diverse datasets. Furthermore, we design a user interface for user-friendly interaction and development with the benchmark for evaluation metrics selection, parameter adjustment, and leaderboard comparison. To ensure accessibility and reproducibility, we opensource our benchmark 3D-FUM and experimental results at https://3dfunctiongroupmoleculedataset.github.io/3D-FuM/#/Home.
Keywords:
Multidisciplinary Topics and Applications: MDA: Physical sciences
Multidisciplinary Topics and Applications: MDA: Bioinformatics