Substructure-Atom Cross Attention for Molecular Representation Learning
- Title
- Substructure-Atom Cross Attention for Molecular Representation Learning
- Authors
- 김지예
- Date Issued
- 2023
- Publisher
- 포항공과대학교
- 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.
- URI
- http://postech.dcollection.net/common/orgView/200000660878
https://oasis.postech.ac.kr/handle/2014.oak/118261
- Article Type
- Thesis
- Files in This Item:
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