Literature DB >> 19530661

Predicting the predictability: a unified approach to the applicability domain problem of QSAR models.

Horvath Dragos1, Marcou Gilles, Varnek Alexandre.   

Abstract

The present work proposes a unified conceptual framework to describe and quantify the important issue of the Applicability Domains (AD) of Quantitative Structure-Activity Relationships (QSARs). AD models are conceived as meta-models micromicro designed to associate an untrustworthiness score to any molecule M subject to property prediction by a QSAR model micro. Untrustworthiness scores or "AD metrics" Psimicro(M) are an expression of the relationship between M (represented by its descriptors in chemical space) and the space zones populated by the training molecules at the basis of model mu. Scores integrating some of the classical AD criteria (similarity-based, box-based) were considered in addition to newly invented terms such as the consensus prediction variance, the dissimilarity to outlier-free training sets, and the correlation breakdown count (the former two being most successful). A loose correlation is expected to exist between this untrustworthiness and the error |Pmicro(M)-Pexpt(M)| affecting the property Pmicro(M) predicted by micro. While high untrustworthiness does not preclude correct predictions, inaccurate predictions at low untrustworthiness must be imperatively avoided. This kind of relationship is characteristic for the Neighborhood Behavior (NB) problem: dissimilar molecule pairs may or may not display similar properties, but similar molecule pairs with different properties are explicitly "forbidden". Therefore, statistical tools developed to tackle this latter aspect were applied and lead to a unified AD metric benchmarking scheme. A first use of untrustworthiness scores resides in prioritization of predictions, without the need to specify a hard AD border. Moreover, if a significant set of external compounds is available, the formalism allows optimal AD borderlines to be fitted. Eventually, consensus AD definitions were built by means of a nonparametric mixing scheme of two AD metrics of comparable quality and shown to outperform their respective parents.

Entities:  

Year:  2009        PMID: 19530661     DOI: 10.1021/ci9000579

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  27 in total

1.  4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

Authors:  Jahan B Ghasemi; Reihaneh Safavi-Sohi; Euzébio G Barbosa
Journal:  Mol Divers       Date:  2011-11-30       Impact factor: 2.943

Review 2.  In-silico approaches to multi-target drug discovery : computer aided multi-target drug design, multi-target virtual screening.

Authors:  Xiao Hua Ma; Zhe Shi; Chunyan Tan; Yuyang Jiang; Mei Lin Go; Boon Chuan Low; Yu Zong Chen
Journal:  Pharm Res       Date:  2010-03-11       Impact factor: 4.200

3.  Rescoring of docking poses under Occam's Razor: are there simpler solutions?

Authors:  Michael Zhenin; Malkeet Singh Bahia; Gilles Marcou; Alexandre Varnek; Hanoch Senderowitz; Dragos Horvath
Journal:  J Comput Aided Mol Des       Date:  2018-09-01       Impact factor: 3.686

4.  Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.

Authors:  Andrey A Toropov; Alla P Toropova; Marco Marzo; Edoardo Carnesecchi; Gianluca Selvestrel; Emilio Benfenati
Journal:  Mol Divers       Date:  2020-04-23       Impact factor: 2.943

5.  Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Authors:  Chin Yee Liew; Yen Ching Lim; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2011-09-06       Impact factor: 3.686

6.  Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug-like Molecules.

Authors:  Hongmao Sun; Henrike Veith; Menghang Xia; Christopher P Austin; Raymond R Tice; Ruili Huang
Journal:  Mol Inform       Date:  2012-10-11       Impact factor: 3.353

7.  Discovery of potent, selective multidrug and toxin extrusion transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug profiling and computational modeling.

Authors:  Matthias B Wittwer; Arik A Zur; Natalia Khuri; Yasuto Kido; Alan Kosaka; Xuexiang Zhang; Kari M Morrissey; Andrej Sali; Yong Huang; Kathleen M Giacomini
Journal:  J Med Chem       Date:  2013-01-22       Impact factor: 7.446

8.  Estimation of the applicability domain of kernel-based machine learning models for virtual screening.

Authors:  Nikolas Fechner; Andreas Jahn; Georg Hinselmann; Andreas Zell
Journal:  J Cheminform       Date:  2010-03-11       Impact factor: 5.514

9.  Binding affinity prediction with property-encoded shape distribution signatures.

Authors:  Sourav Das; Michael P Krein; Curt M Breneman
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

10.  Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration.

Authors:  Katsuhisa Matsumoto; Tomoyuki Miyao; Kimito Funatsu
Journal:  ACS Omega       Date:  2021-04-28
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