Literature DB >> 22458680

Analysis of structure-based virtual screening studies and characterization of identified active compounds.

Peter Ripphausen1, Dagmar Stumpfe, Jürgen Bajorath.   

Abstract

Structure-based virtual screening makes explicit or implicit use of 3D target structure information to detect novel active compounds. Results of nearly 300 currently available original applications have been analyzed to characterize the state-of-the-art in this field. Compound selection from docking calculations is much influenced by subjective criteria. Although submicromolar compounds are identified, the majority of docking hits are only weakly potent. However, only a small percentage of docking hits can be reproduced by ligand-based methods. When docking calculations identify potent hits, they often originate from specialized compound sources (e.g., pharmaceutical compound decks or target-focused libraries) and also display a notable bias towards kinase targets. Structure-based virtual screening is the dominant approach to computational hit identification. Docking calculations frequently identify active compounds. Limited accuracy of compound scoring and ranking currently presents a major caveat of the approach that is often compensated for by chemical intuition and knowledge.

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Year:  2012        PMID: 22458680     DOI: 10.4155/fmc.12.18

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  8 in total

Review 1.  Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.

Authors:  Bruno O Villoutreix; Melaine A Kuenemann; Jean-Luc Poyet; Heriberto Bruzzoni-Giovanelli; Céline Labbé; David Lagorce; Olivier Sperandio; Maria A Miteva
Journal:  Mol Inform       Date:  2014-06-02       Impact factor: 3.353

2.  Classifiers and their Metrics Quantified.

Authors:  J B Brown
Journal:  Mol Inform       Date:  2018-01-23       Impact factor: 3.353

3.  Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening.

Authors:  Jean-Paul Ebejer; Paul W Finn; Wing Ki Wong; Charlotte M Deane; Garrett M Morris
Journal:  J Chem Inf Model       Date:  2019-06-04       Impact factor: 4.956

Review 4.  Mimicking Strategy for Protein-Protein Interaction Inhibitor Discovery by Virtual Screening.

Authors:  Ke-Jia Wu; Pui-Man Lei; Hao Liu; Chun Wu; Chung-Hang Leung; Dik-Lung Ma
Journal:  Molecules       Date:  2019-12-04       Impact factor: 4.411

5.  Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Authors:  Lun K Tsou; Shiu-Hwa Yeh; Shau-Hua Ueng; Chun-Ping Chang; Jen-Shin Song; Mine-Hsine Wu; Hsiao-Fu Chang; Sheng-Ren Chen; Chuan Shih; Chiung-Tong Chen; Yi-Yu Ke
Journal:  Sci Rep       Date:  2020-10-08       Impact factor: 4.379

6.  SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors.

Authors:  Surendra Kumar; Mi-Hyun Kim
Journal:  J Cheminform       Date:  2021-03-25       Impact factor: 5.514

7.  In silico approach for the discovery of new PPARγ modulators among plant-derived polyphenols.

Authors:  José Antonio Encinar; Gregorio Fernández-Ballester; Vicente Galiano-Ibarra; Vicente Micol
Journal:  Drug Des Devel Ther       Date:  2015-11-04       Impact factor: 4.162

Review 8.  Computer-aided drug discovery.

Authors:  Jürgen Bajorath
Journal:  F1000Res       Date:  2015-08-26
  8 in total

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