Molecular property prediction offers an effective and efficient approach for
early screening and optimization of drug candidates. Although deep learning
based methods have made notable progress, most existing works still do not
fully utilize 3D spatial information. This can lead to a single molecular
representation representing multiple actual molecules. To address these issues,
we propose a novel 3D structure-based molecular modeling method named 3D-Mol.
In order to accurately represent complete spatial structure, we design a novel
encoder to extract 3D features by deconstructing the molecules into three
geometric graphs. In addition, we use 20M unlabeled data to pretrain our model
by contrastive learning. We consider conformations with the same topological
structure as positive pairs and the opposites as negative pairs, while the
weight is determined by the dissimilarity between the conformations. We compare
3D-Mol with various state-of-the-art (SOTA) baselines on 7 benchmarks and
demonstrate our outstanding performance in 5 benchmarks.

通过提取三维特征和使用对比学习的方法，我们的研究提出了一种名为 3D-Mol 的新型三维结构分子建模方法，用于准确表示空间结构，并在 7 个基准测试中展示了出色的性能。