Literature DB >> 22822717

How similar are those molecules after all? Use two descriptors and you will have three different answers.

Andreas Bender1.   

Abstract

IMPORTANCE OF THE FIELD: Molecular similarity searching (ligand-based virtual screening) is one of the routine computational techniques used in drug discovery and pharmacological research. However, while a large number of descriptors exist, there are no general guidelines whatsoever which descriptors work better and which descriptors should be used in the different cases. AREAS COVERED IN THIS REVIEW: This review provides a brief overview of current molecular descriptors and databases used for their evaluation, followed by a critical discussion of their differences. WHAT THE READER WILL GAIN: After reading this review, the reader will be aware of how very differently molecular descriptors assess similarities of molecules, and the performance that can be realistically expected from them. TAKE HOME MESSAGE: Molecular descriptors come in a variety of forms, and they show vast differences in assessing the similarity between molecules. Virtual screening performance of many descriptors is often lower than expected, compared to 'dumb' descriptors while some simple methods such as circular fingerprints offer surprisingly good performance in many cases. The choice of the right benchmark library is crucial, many of which are summarized in this review.

Year:  2010        PMID: 22822717     DOI: 10.1517/17460441.2010.517832

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  10 in total

1.  Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models.

Authors:  Vinicius M Alves; Alexander Golbraikh; Stephen J Capuzzi; Kammy Liu; Wai In Lam; Daniel Robert Korn; Diane Pozefsky; Carolina Horta Andrade; Eugene N Muratov; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2018-06-13       Impact factor: 4.956

2.  Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

Authors:  José L Medina-Franco; Bruce S Edwards; Clemencia Pinilla; Jon R Appel; Marc A Giulianotti; Radleigh G Santos; Austin B Yongye; Larry A Sklar; Richard A Houghten
Journal:  J Chem Inf Model       Date:  2013-06-07       Impact factor: 4.956

3.  Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision.

Authors:  John D Holliday; Evangelos Kanoulas; Nurul Malim; Peter Willett
Journal:  J Cheminform       Date:  2011-08-08       Impact factor: 5.514

4.  Understanding and classifying metabolite space and metabolite-likeness.

Authors:  Julio E Peironcely; Theo Reijmers; Leon Coulier; Andreas Bender; Thomas Hankemeier
Journal:  PLoS One       Date:  2011-12-14       Impact factor: 3.240

Review 5.  Fusing similarity rankings in ligand-based virtual screening.

Authors:  Peter Willett
Journal:  Comput Struct Biotechnol J       Date:  2013-02-24       Impact factor: 7.271

6.  A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

Authors:  Steve O Hagan; Neil Swainston; Julia Handl; Douglas B Kell
Journal:  Metabolomics       Date:  2014-09-19       Impact factor: 4.290

7.  Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria.

Authors:  Sereina Riniker; Gregory A Landrum; Floriane Montanari; Santiago D Villalba; Julie Maier; Johanna M Jansen; W Patrick Walters; Anang A Shelat
Journal:  F1000Res       Date:  2017-07-17

8.  Open-source platform to benchmark fingerprints for ligand-based virtual screening.

Authors:  Sereina Riniker; Gregory A Landrum
Journal:  J Cheminform       Date:  2013-05-30       Impact factor: 5.514

9.  On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Authors:  Rajarshi Guha; José L Medina-Franco
Journal:  J Cheminform       Date:  2014-04-02       Impact factor: 5.514

10.  3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery.

Authors:  Albert J Kooistra; Márton Vass; Ross McGuire; Rob Leurs; Iwan J P de Esch; Gert Vriend; Stefan Verhoeven; Chris de Graaf
Journal:  ChemMedChem       Date:  2018-02-14       Impact factor: 3.466

  10 in total

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