A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning

• URL: http://arxiv.org/abs/2211.02130v1
• Date: Thu, 3 Nov 2022 20:10:46 GMT
• Title: A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning
• Authors: Austin Atsango, Nathaniel L. Diamant, Ziqing Lu, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang
• Abstract summary: We propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS) Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape.
• Score: 0.7340017786387767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.

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