Enhancing Molecular Representation Learning Through the Combination of 3D and 2D Graph Machine Learning (Student Abstract)

Abstract Molecular machine learning has broad applications across multiple domains such as drug development, environmental toxicology, and materials science. Various pre-trained frameworks using self-supervised representation learning have emerged to tackle the difficulty of obtaining large molecular datasets useful for training high-performing molecular machine learning models. In this study, we explore a novel representation learning framework trained using both 2D and 3D molecular data. Specifically, a 3D invariant graph neural network to learn how to capture 3D atomic information and then pass these atomic representations into a regular 2D graph neural network which can leverage molecular topology. Results from experiments demonstrate the representations produced by our method using both 3D and 2D molecular information lead to strong performance in downstream tasks.