Literature DB >> 27467159

Interpretation of QSAR Models Based on Random Forest Methods.

Victor E Kuz'min1, Pavel G Polishchuk2, Anatoly G Artemenko1, Sergey A Andronati1.   

Abstract

A new algorithm for the interpretation of Random Forest models has been developed. It allows to calculate the contribution of each descriptor to the calculated property value. In case of the simplex representation of a molecular structure, contributions of individual atoms can be calculated, and thus it becomes possible to estimate the influence of separate molecular fragments on the investigated property. Such information can be used for the design of new compounds with a predefined property value. The proposed measure of descriptor contributions is not an alternative to the importance of Breiman's variable, but it characterizes the contribution of a particular explanatory variable to the calculated response value.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Random Forest interpretation; Simplex representation of molecular structure

Year:  2011        PMID: 27467159     DOI: 10.1002/minf.201000173

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  6 in total

1.  QSAR modeling: where have you been? Where are you going to?

Authors:  Artem Cherkasov; Eugene N Muratov; Denis Fourches; Alexandre Varnek; Igor I Baskin; Mark Cronin; John Dearden; Paola Gramatica; Yvonne C Martin; Roberto Todeschini; Viviana Consonni; Victor E Kuz'min; Richard Cramer; Romualdo Benigni; Chihae Yang; James Rathman; Lothar Terfloth; Johann Gasteiger; Ann Richard; Alexander Tropsha
Journal:  J Med Chem       Date:  2014-01-06       Impact factor: 7.446

2.  Alarms about structural alerts.

Authors:  Vinicius Alves; Eugene Muratov; Stephen Capuzzi; Regina Politi; Yen Low; Rodolpho Braga; Alexey V Zakharov; Alexander Sedykh; Elena Mokshyna; Sherif Farag; Carolina Andrade; Victor Kuz'min; Denis Fourches; Alexander Tropsha
Journal:  Green Chem       Date:  2016-06-28       Impact factor: 10.182

3.  QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction.

Authors:  Isidro Cortés-Ciriano; Ctibor Škuta; Andreas Bender; Daniel Svozil
Journal:  J Cheminform       Date:  2020-06-05       Impact factor: 5.514

4.  Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks.

Authors:  Demetrius DiMucci; Mark Kon; Daniel Segrè
Journal:  mSystems       Date:  2018-10-30       Impact factor: 6.496

5.  Machine learning methods in chemoinformatics.

Authors:  John B O Mitchell
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2014-09-01

6.  Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure-activity relationship approaches.

Authors:  Hayriye Yilmaz; Natalia Sizochenko; Bakhtiyor Rasulev; Andrey Toropov; Yahya Guzel; Viktor Kuz'min; Danuta Leszczynska; Jerzy Leszczynski
Journal:  J Food Drug Anal       Date:  2015-04-01       Impact factor: 6.157

  6 in total

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