Developing a hybrid screening method based on machine learning with Ligand database

• Project/Area Number: 15K00408
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Life / Health / Medical informatics
• Research Institution: Tokyo University of Science
• Principal Investigator: Hayato Ohwada 東京理科大学, 理工学部経営工学科, 教授 (30203954)
• Co-Investigator(Kenkyū-buntansha): 青木 伸 東京理科大学, 薬学部生命創薬科学科, 教授 (00222472) ; 西山 裕之 東京理科大学, 理工学部経営工学科, 教授 (80328567)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Project Status: Completed (Fiscal Year 2017)
• Budget Amount: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000); Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000); Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000); Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
• Keywords: 機械学習 / 化合物スクリーニング / リガンドデータベース / バイオインフォマティクス / スクリーニング

Outline of Final Research Achievements

This research focuses on a hybrid machine learning method to predict chemical properties of drug candidates using ligand databases. In-silico screening is a promising selection method for drug discovery, we have combined support vector machines with inductive logic programming, yieding a new method for improving the predictive accuracy for drug candidate selection. Moreover, p53 targeting radio protective compounds are predicted to decrease the side effect of radio based therapy. The outcomes are presented at a journal and international conference proceedings.

Research Products

• [Journal Article] Enzyme Classification on DUD-E Database Using Logistic Regression Ensemble (Lorens) (2018)
  • Author(s): Kuswanto H., Melasasi J.N., Ohwada H
  • Journal Title: Innovative Computing, Optimization and Its Applications. Studies in Computational Intelligence Volume: 741 Pages: 93-109
  • DOI: 10.1007/978-3-319-66984-7_6
  • ISBN: 9783319669830, 9783319669847
  • Peer Reviewed
• [Journal Article] Predicting radiation protection and toxicity of p53 targeting radioprotectors using machine learning (2017)
  • Author(s): Masataka Kimura, Shin Aoki, Hayato Ohwada
  • Journal Title: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology Volume: - Pages: 1-6
  • DOI: 10.1109/cibcb.2017.8058540
  • Peer Reviewed
• [Journal Article] Docking Score Calculation Using Machine Learning with an Enhanced Inhibitor Database (2015)
  • Author(s): Masato Okada, Tadasuke Ito, Hayato Ohwada and Shin Aoki
  • Journal Title: Journal of Medical Imaging and Health Informatics Volume: 5 Issue: 5 Pages: 1104-1107
  • DOI: 10.1166/jmihi.2015.1503
  • Peer Reviewed
• [Presentation] Comparison of Random Forest and SVM for the Raw Data in Drug Discovery: Prediction of Radiation Protection and Toxicity Case Study (2016)
  • Author(s): Atsushi Matsumoto, Shin Aoki, and Hayato Ohwada
  • Organizer: 8th International Conference on Machine Learning and Computing
  • Place of Presentation: REGAL ORIENTAL HOTEL,Kowloon City, Hong Kong
  • Year and Date: 2016-02-22
  • Int'l Joint Research
• [Presentation] In silico Screening of Zinc(II) Enzyme Inhibitors by SVM (2015)
  • Author(s): Tadasuke Ito, Masato Okada, Shotaro Togami, Shin Aoki and Hayato Ohwada
  • Organizer: 6th international conference on Computational Systems-Biology and Bioinformatics
  • Place of Presentation: Chatrium Hotel Riverside Bangkok,Bangkok,Thailand
  • Year and Date: 2015-11-22
  • Int'l Joint Research
• [Presentation] Prediction of Radioprotectiors Targeting p53 for Suppression of Acute Effect of Cancer Radiotherapy using Machine Learning (2015)
  • Author(s): Atsushi Matsumoto,Tadasuke Ito,Yurie Nishi,Shinya Ariyasu,Shin Aoki and Hayato Ohwada
  • Organizer: The IEEE International Conference on Bioinformatics and Biomedicine 2015
  • Place of Presentation: Hyatt Regency Bethesda, Bethesda, Maryland 20814, USA
  • Year and Date: 2015-11-09
  • Int'l Joint Research
• [Presentation] ILP based screening applied to predicting carbonic anhydrase II ligands (2015)
  • Author(s): Tadasuke Ito, Masato Okada, Shotaro Togami, Shinya Ariyasu, Shin Aoki, and Hayato Ohwada
  • Organizer: The IEEE International Conference on Bioinformatics and Biomedicine 2015
  • Place of Presentation: Hyatt Regency Bethesda, Bethesda, Maryland 20814, USA
  • Year and Date: 2015-11-09
  • Int'l Joint Research
• [Presentation] In Silico Screening of Zinc(II) Enzyme Inhibitors by ILP (2015)
  • Author(s): Tadasuke Ito, Shotaro Togami, Shin Aoki and Hayato Ohwada
  • Organizer: 25th International Conference on Inductive Logic Programming
  • Place of Presentation: Kyoto University Raku-Yu. Kaikan, University of Kyoto,Japan
  • Year and Date: 2015-08-20
  • Int'l Joint Research