Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention
- 주제(기타) Chemistry, Medicinal
- 주제(기타) Chemistry, Multidisciplinary
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Computer Science, Interdisciplinary Applications
- 설명문(일반) [Kim, Hyunseung; Lee, Won Bo] Seoul Natl Univ, Sch Chem & Biol Engn, Seoul 08826, South Korea; [Na, Jonggeol] Ewha Womans Univ, Dept Chem Engn & Mat Sci, Grad Program Syst Hlth Sci & Engn, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 Green Submitted
- 발행기관 AMER CHEMICAL SOC
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000191095
- 본문언어 영어
- Published As https://doi.org/10.1021/acs.jcim.1c01289
- PubMed https://pubmed.ncbi.nlm.nih.gov/34855384
초록/요약
Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding of chemical language is proposed (generative chemical Transformer, GCT). The attention mechanism in GCT allows a deeper understanding of molecular structures beyond the limitations of chemical language itself which cause semantic discontinuity by paying attention to characters sparsely. The significance of language models for inverse molecular design problems is investigated by quantitatively evaluating the quality of the generated molecules. GCT generates highly realistic chemical strings that satisfy both chemical and linguistic grammar rules. Molecules parsed from the generated strings simultaneously satisfy the multiple target properties and vary for a single condition set. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.
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