Literature DB >> 15556481

Development of KiBank, a database supporting structure-based drug design.

Junwei Zhang1, Masahiro Aizawa, Shinji Amari, Yoshio Iwasawa, Tatsuya Nakano, Kotoko Nakata.   

Abstract

KiBank is a database of inhibition constant (Ki) values with 3D structures of target proteins and chemicals. Ki values were accumulated from peer-reviewed literature searched via PubMed. The 3D structure files of target proteins were originally from Protein Data Bank (PDB), while the 2D structure files of the chemicals were collected together with the Ki values and then converted into 3D ones. In KiBank, the chemical and protein 3D structures with hydrogen atoms were optimized by energy minimization and stored in MDL MOL and PDB format, respectively. KiBank is designed to support structure-based drug design. It provides structure files of proteins and chemicals ready for use in virtual screening through automated docking methods, while the Ki values can be applied for tests of docking/scoring combinations, program parameter settings, and calibration of empirical scoring functions. Additionally, the chemical structures and corresponding Ki values in KiBank are useful for lead optimization based on quantitative structure-activity relationship (QSAR) techniques. KiBank is updated on a daily basis and is freely available at . As of August 2004, KiBank contains 8000 Ki values, over 6000 chemicals and 166 proteins covering the subtypes of receptors and enzymes.

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Year:  2004        PMID: 15556481     DOI: 10.1016/j.compbiolchem.2004.09.003

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  9 in total

1.  Benchmarking sets for molecular docking.

Authors:  Niu Huang; Brian K Shoichet; John J Irwin
Journal:  J Med Chem       Date:  2006-11-16       Impact factor: 7.446

Review 2.  Community benchmarks for virtual screening.

Authors:  John J Irwin
Journal:  J Comput Aided Mol Des       Date:  2008-02-14       Impact factor: 3.686

3.  Improving performance of docking-based virtual screening by structural filtration.

Authors:  Fedor N Novikov; Viktor S Stroylov; Oleg V Stroganov; Ghermes G Chilov
Journal:  J Mol Model       Date:  2009-12-30       Impact factor: 1.810

4.  AffinDB: a freely accessible database of affinities for protein-ligand complexes from the PDB.

Authors:  Peter Block; Christoph A Sotriffer; Ingo Dramburg; Gerhard Klebe
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

5.  BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities.

Authors:  Tiqing Liu; Yuhmei Lin; Xin Wen; Robert N Jorissen; Michael K Gilson
Journal:  Nucleic Acids Res       Date:  2006-12-01       Impact factor: 16.971

6.  FAF-Drugs: free ADME/tox filtering of compound collections.

Authors:  Maria A Miteva; Stephanie Violas; Matthieu Montes; David Gomez; Pierre Tuffery; Bruno O Villoutreix
Journal:  Nucleic Acids Res       Date:  2006-07-01       Impact factor: 16.971

7.  Binding MOAD, a high-quality protein-ligand database.

Authors:  Mark L Benson; Richard D Smith; Nickolay A Khazanov; Brandon Dimcheff; John Beaver; Peter Dresslar; Jason Nerothin; Heather A Carlson
Journal:  Nucleic Acids Res       Date:  2007-11-30       Impact factor: 16.971

8.  Predicting the protein targets for athletic performance-enhancing substances.

Authors:  Lazaros Mavridis; John Bo Mitchell
Journal:  J Cheminform       Date:  2013-06-25       Impact factor: 5.514

9.  Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

Authors:  Abraham Yosipof; Rita C Guedes; Alfonso T García-Sosa
Journal:  Front Chem       Date:  2018-05-09       Impact factor: 5.221

  9 in total

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