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

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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