Substructure-Atom Cross Attention for Molecular Representation LearningDownload PDF

Published: 21 Oct 2022, Last Modified: 01 Oct 2023AI4Science PosterReaders: Everyone
Keywords: Molecule representation learning
TL;DR: This paper proposes a novel framework that incorporates molecular substructure information to node-wise features effectively.
Abstract: Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design. In this work, we propose a new framework for molecular representation learning. Our contribution is threefold: (a) demonstrating the usefulness of incorporating substructures to node-wise features from molecules, (b) designing two branch networks consisting of a transformer and a graph neural network so that the networks fused with asymmetric attention, and (c) not requiring heuristic features and computationally-expensive information from molecules. Using 1.8 million molecules collected from ChEMBL and PubChem database, we pretrain our network to learn a general representation of molecules with minimal supervision. The experimental results show that our pretrained network achieves competitive performance on 11 downstream tasks for molecular property prediction.
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