Tetrahedral molecular pretraining for enhanced property prediction
Year: 2025
Type: article
Abstract: Self-supervised learning on 3D molecular structures has emerged as a promising direction in data-driven scientific research, addressing the significant challenge of limited annotated biochemical data.While the identification of semantic units for pretraining has been well-established in natural language processing and computer vision, determining optimal building blocks for characterizing 3D molecular architectures remains underexplored.We present Tetrahedral Molecular Pretraining (TMP), a novel approach that recognizes tetrahedrons as fundamental building blocks, leveraging their geometric simplicity and recurring presence across chemical functional groups.Through systematic perturbation and reconstruction of tetrahedral substructures, TMP implements a self-supervised learning strategy that recovers both their global arrangements and local patterns, learning rich molecular representations that encode multi-scale structural information.Extensive evaluations on 24 benchmark datasets demonstrate that TMP consistently outperforms existing methods in tasks ranging from biochemical property prediction to quantum property prediction.Notably, the tetrahedra-based modeling successfully scales from small molecules to complex protein-ligand systems, achieving new state-of-the-art results in binding affinity prediction.Our findings highlight how the identification of representative structural patterns can lead to more expressive and interpretable neural networks for scientific applications.
Source: Pattern Recognition
Authors: Yuancheng Sun, Kai Chen, Kang Liu, Qiwei Ye
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,590
Award: G374226651
OpenAlex ID: W4415528906