Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target
Year: 2015
Type: article
Abstract: A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.
Source: Scientific Reports
DOI: 10.1038/srep17209
Authors: Shuntaro Chiba, Kazuyoshi Ikeda, Takashi Ishida, M. Michael Gromiha, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Kun-Yi Hsin, Hiroaki Kitano, Kazuki Yamamoto, Nobuyoshi Sugaya, Koya Kato, Tatsuya Okuno, George Chikenji, Masahiro Mochizuki, Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban, Reiji Teramoto, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, Philip Prathipati, Junichi Ito, Yuko Tsuchiya, Kenji Mizuguchi, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima
Institutions: Tokyo Institute of Technology, Indian Institute of Technology Madras, Chuo University, Okinawa Institute of Science and Technology Graduate University, RIKEN, Systems Biology Institute, RIKEN Center for Integrative Medical Sciences, Research Center for Advanced Science and Technology, The University of Tokyo, The University of Tokyo, Nagoya University, Information and Mathematical Science and Bioinformatics (Japan), University of Madras, National Institute of Biomedical Innovation, Health and Nutrition, National Institute of Advanced Industrial Science and Technology
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Development of an intelligent dynamic docking pipeline for improving molecular docking simulations., 立体構造情報と相互作用情報を組み合わせた薬剤オフターゲット予測システムの開発, In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease, G4416956273, Computational drug discovery techniques with systematic target identification and high-performance molecular simulation
OpenAlex ID: W2178762325