Deep Neural Networks for Knowledge-Enhanced Molecular Modeling

Siyu Long, Jianyu Wu, Yi Zhou, Fan Sha, Xinyu Dai

Designing neural networks for molecular modeling is a crucial task in the field of artificial intelligence. The goal is to utilize neural networks to understand and design molecules, which has significant implications for drug development and other real-world applications. Recently, with the advancement of deep learning, molecular modeling has made considerable progress. However, current methods are primarily data-driven, overlooking the role of domain knowledge, such as molecular shapes, in the modeling process. In this paper, we systematically investigate how incorporating molecular shape knowledge can enhance molecular modeling. Specifically, we design two deep neural networks, ShapePred and ShapeGen, to utilize molecular shapes in molecule prediction and generation. Experimental results demonstrate that integrating shape knowledge can significantly improve model performance. Notably, ShapePred exhibits strong performance across 11 molecule prediction datasets, while ShapeGen can more efficiently generate high-quality drug molecules based on given target proteins.

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