Regression Models and Ranking Method for p53 Inhibitor Candidates Using Machine Learning

Haruka Motohashi, Tatsuro Teraoka, Shin Aoki, Hayato Ohwada

研究成果: Conference contribution査読

2 被引用数 (Scopus)

抄録

Radiation therapy is one of the main treatments for cancer. However, it may cause various side effects owing to the apoptosis activity of the p53 protein in normal cells. Therefore, to avoid the side effects, it is important to protect normal cells against radiation by using p53 inhibitors. It is also expected that p53 inhibitors have low toxicity against patients' bodies. However, the design of p53 inhibitors is not easy because drug discovery requires enormous costs and long time. In this paper, we propose a new method for ranking candidate p53 inhibitors, considering both their radioprotective function and cytotoxicity. We use features of the two- and three-dimensional structures of the compounds, including fingerprints, some machine learning methods such as random forest and SVR (Support Vector Machine), and one method for ranking, i.e., the Pareto ranking method. Therefore, we present the regression models of the cytotoxicity and radioprotective functions of the candidates to determine their ranking. Our proposed methods yield useful rankings for drug discovery.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
編集者Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
出版社Institute of Electrical and Electronics Engineers Inc.
ページ708-712
ページ数5
ISBN(電子版)9781538654880
DOI
出版ステータスPublished - 21 1月 2019
イベント2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
継続期間: 3 12月 20186 12月 2018

出版物シリーズ

名前Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
国/地域Spain
CityMadrid
Period3/12/186/12/18

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