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.
@article{arxiv.2211.02130,
title = {A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning},
author = {Austin Atsango and Nathaniel L. Diamant and Ziqing Lu and Tommaso Biancalani and Gabriele Scalia and Kangway V. Chuang},
journal= {arXiv preprint arXiv:2211.02130},
year = {2022}
}