Literature DB >> 27477814

Interpretability of SAR/QSAR Models of any Complexity by Atomic Contributions.

G Marcou1, D Horvath1, V Solov'ev1,2, A Arrault3, P Vayer3, A Varnek4.   

Abstract

Entities:  

Keywords:  Acetylcholinesterase inhibitors; Aqueous solubility; Atom colouring; Atomic contributions; Chemoinformatics; Fragment molecular descriptors; Organoleptics; QSAR

Year:  2012        PMID： 27477814     DOI： 10.1002/minf.201100136

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


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  4 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.  BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.

Authors:  Oanh Vu; Jeffrey Mendenhall; Doaa Altarawy; Jens Meiler
Journal:  J Comput Aided Mol Des       Date:  2019-04-06       Impact factor: 3.686

3.  e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

Authors:  Suqing Zheng; Mengying Jiang; Chengwei Zhao; Rui Zhu; Zhicheng Hu; Yong Xu; Fu Lin
Journal:  Front Chem       Date:  2018-03-29       Impact factor: 5.221

4.  Explainable machine learning predictions of dual-target compounds reveal characteristic structural features.

Authors:  Christian Feldmann; Maren Philipps; Jürgen Bajorath
Journal:  Sci Rep       Date:  2021-11-03       Impact factor: 4.379
  4 in total

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