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Substructure-Atom Cross Attention for Molecular Representation Learning
Jiye Kim · Seungbeom Lee · Dongwoo Kim · Sungsoo Ahn · Jaesik Park

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in AI for Science: Progress and Promises »
Event URL: https://openreview.net/forum?id=qdCa5rfUnp »

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.

Keywords: Molecule Representation Learning

Author Information

Jiye Kim (POSTECH)
Seungbeom Lee (Pohang University of Science and Technology)
Dongwoo Kim (POSTECH)
Sungsoo Ahn (POSTECH)
Jaesik Park (POSTECH)

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