  Molecular property prediction, crucial for early drug candidate screening and
optimization, has seen advancements with deep learning-based methods. While
deep learning-based methods have advanced considerably, they often fall short
in fully leveraging 3D spatial information. Specifically, current molecular
encoding techniques tend to inadequately extract spatial information, leading
to ambiguous representations where a single one might represent multiple
distinct molecules. Moreover, existing molecular modeling methods focus
predominantly on the most stable 3D conformations, neglecting other viable
conformations present in reality. To address these issues, we propose 3D-Mol, a
novel approach designed for more accurate spatial structure representation. It
deconstructs molecules into three hierarchical graphs to better extract
geometric information. Additionally, 3D-Mol leverages contrastive learning for
pretraining on 20 million unlabeled data, treating their conformations with
identical topological structures as weighted positive pairs and contrasting
ones as negatives, based on the similarity of their 3D conformation descriptors
and fingerprints. We compare 3D-Mol with various state-of-the-art baselines on
7 benchmarks and demonstrate our outstanding performance.
