DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김지예 | - |
dc.date.accessioned | 2023-08-31T16:31:37Z | - |
dc.date.available | 2023-08-31T16:31:37Z | - |
dc.date.issued | 2023 | - |
dc.identifier.other | OAK-2015-10064 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000660878 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/118261 | - |
dc.description | Master | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | Substructure-Atom Cross Attention for Molecular Representation Learning | - |
dc.title.alternative | 분자 표현학습을 위한 하부구조와 원자간 상호 어텐션 네트워크 | - |
dc.type | Thesis | - |
dc.contributor.college | 인공지능대학원 | - |
dc.date.degree | 2023- 2 | - |
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