Literature DB >> 22257668

COPICAT: a software system for predicting interactions between proteins and chemical compounds.

Yasubumi Sakakibara1, Tsuyoshi Hachiya, Miho Uchida, Nobuyoshi Nagamine, Yohei Sugawara, Masahiro Yokota, Masaomi Nakamura, Kris Popendorf, Takashi Komori, Kengo Sato.   

Abstract

UNLABELLED: Since tens of millions of chemical compounds have been accumulated in public chemical databases, fast comprehensive computational methods to predict interactions between chemical compounds and proteins are needed for virtual screening of lead compounds. Previously, we proposed a novel method for predicting protein-chemical interactions using two-layer Support Vector Machine classifiers that require only readily available biochemical data, i.e. amino acid sequences of proteins and structure formulas of chemical compounds. In this article, the method has been implemented as the COPICAT web service, with an easy-to-use front-end interface. Users can simply submit a protein-chemical interaction prediction job using a pre-trained classifier, or can even train their own classification model by uploading training data. COPICAT's fast and accurate computational prediction has enhanced lead compound discovery against a database of tens of millions of chemical compounds, implying that the search space for drug discovery is extended by >1000 times compared with currently well-used high-throughput screening methodologies. AVAILABILITY: The COPICAT server is available at http://copicat.dna.bio.keio.ac.jp. All functions, including the prediction function are freely available via anonymous login without registration. Registered users, however, can use the system more intensively.

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Year:  2012        PMID: 22257668     DOI: 10.1093/bioinformatics/bts031

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds.

Authors:  Masaomi Nakamura; Tsuyoshi Hachiya; Yutaka Saito; Kengo Sato; Yasubumi Sakakibara
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

2.  Nordihydroguaiaretic Acid Disrupts the Antioxidant Ability of Helicobacter pylori through the Repression of SodB Activity In Vitro.

Authors:  Hitoshi Tsugawa; Hideki Mori; Juntaro Matsuzaki; Tatsuhiro Masaoka; Tasuku Hirayama; Hideko Nagasawa; Yasubumi Sakakibara; Makoto Suematsu; Hidekazu Suzuki
Journal:  Biomed Res Int       Date:  2015-04-06       Impact factor: 3.411

3.  Mining Chemical Activity Status from High-Throughput Screening Assays.

Authors:  Othman Soufan; Wail Ba-alawi; Moataz Afeef; Magbubah Essack; Valentin Rodionov; Panos Kalnis; Vladimir B Bajic
Journal:  PLoS One       Date:  2015-12-14       Impact factor: 3.240

4.  DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.

Authors:  Heng Luo; Ping Zhang; Xi Hang Cao; Dizheng Du; Hao Ye; Hui Huang; Can Li; Shengying Qin; Chunling Wan; Leming Shi; Lin He; Lun Yang
Journal:  Sci Rep       Date:  2016-11-02       Impact factor: 4.379

5.  DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning.

Authors:  Othman Soufan; Wail Ba-Alawi; Moataz Afeef; Magbubah Essack; Panos Kalnis; Vladimir B Bajic
Journal:  J Cheminform       Date:  2016-11-10       Impact factor: 5.514

Review 6.  Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Authors:  Maryam Bagherian; Elyas Sabeti; Kai Wang; Maureen A Sartor; Zaneta Nikolovska-Coleska; Kayvan Najarian
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

7.  Toward more realistic drug-target interaction predictions.

Authors:  Tapio Pahikkala; Antti Airola; Sami Pietilä; Sushil Shakyawar; Agnieszka Szwajda; Jing Tang; Tero Aittokallio
Journal:  Brief Bioinform       Date:  2014-04-09       Impact factor: 11.622

8.  DINIES: drug-target interaction network inference engine based on supervised analysis.

Authors:  Yoshihiro Yamanishi; Masaaki Kotera; Yuki Moriya; Ryusuke Sawada; Minoru Kanehisa; Susumu Goto
Journal:  Nucleic Acids Res       Date:  2014-05-16       Impact factor: 16.971

9.  DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.

Authors:  Othman Soufan; Wail Ba-Alawi; Arturo Magana-Mora; Magbubah Essack; Vladimir B Bajic
Journal:  Sci Rep       Date:  2018-06-14       Impact factor: 4.379

10.  Deep learning integration of molecular and interactome data for protein-compound interaction prediction.

Authors:  Narumi Watanabe; Yuuto Ohnuki; Yasubumi Sakakibara
Journal:  J Cheminform       Date:  2021-05-01       Impact factor: 8.489

  10 in total

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