- We are delighted to annouce that our paper is accepted by International Joint Conferences on Artificial Intelligence (IJCAI) 2024
- Abstract: 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.
- Contribution 1: Introduce a comprehensive molecular learning benchmark based on functional groups for 3D molecule learning (namely 3D-FUM) based on the QM9 dataset and domain knowledge of functional groups in organic chemistry
- Contribution 2: Summarize mainstream evaluation metrics and task for Ai-assisted molecular research.
- Contribution 3: Establish a leaderboard to track the latest advancements in using AI models for molecular research.
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