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タイトル: | Prediction of pharmacological activities from chemical structures with graph convolutional neural networks |
著者: | Sakai, Miyuki Nagayasu, Kazuki https://orcid.org/0000-0002-7438-732X (unconfirmed) Shibui, Norihiro Andoh, Chihiro Takayama, Kaito Shirakawa, Hisashi https://orcid.org/0000-0002-4129-0978 (unconfirmed) Kaneko, Shuji https://orcid.org/0000-0001-5152-5809 (unconfirmed) |
著者名の別形: | 酒井, 幸 永安, 一樹 澁井, 紀宏 安藤, 千紘 高山, 海都 白川, 久志 金子, 周司 |
キーワード: | Data mining Drug safety Drug screening High-throughput screening Machine learning Neuroscience Pharmaceutics Pharmacology Toxicology Transporters in the nervous system Virtual drug screening |
発行日: | 12-Jan-2021 |
出版者: | Springer Nature |
誌名: | Scientific Reports |
巻: | 11 |
論文番号: | 525 |
抄録: | Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies. |
記述: | 化合物の薬理作用を予測する技術を開発 --薬理作用ビッグデータを用いて--. 京都大学プレスリリース. 2021-01-13. |
著作権等: | © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
URI: | http://hdl.handle.net/2433/260969 |
DOI(出版社版): | 10.1038/s41598-020-80113-7 |
PubMed ID: | 33436854 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2021-01-13 |
出現コレクション: | 学術雑誌掲載論文等 |
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