Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure–Activity RelationshipsClick to copy article linkArticle link copied!
- Yen S. LowYen S. LowLaboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United StatesMore by Yen S. Low
- Vinicius M. AlvesVinicius M. AlvesLaboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United StatesLaboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, Goias 74605-170, BrazilMore by Vinicius M. Alves
- Denis FourchesDenis FourchesDepartment of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United StatesMore by Denis Fourches
- Alexander SedykhAlexander SedykhSciome LLC, Research Triangle Park, North Carolina 27709, United StatesMore by Alexander Sedykh
- Carolina Horta AndradeCarolina Horta AndradeLaboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, Goias 74605-170, BrazilMore by Carolina Horta Andrade
- Eugene N. MuratovEugene N. MuratovLaboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United StatesDepartment of Chemical Technology, Odessa National Polytechnic University, Odessa 65000, UkraineMore by Eugene N. Muratov
- Ivan RusynIvan RusynDepartment of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United StatesMore by Ivan Rusyn
- Alexander Tropsha*Alexander Tropsha*Phone: (919) 966-2955; Fax: (919) 966-0204; E-mail: [email protected]Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United StatesMore by Alexander Tropsha
Abstract
Quantitative structure–activity relationships (QSAR) models are often seen as a “black box” because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens–Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.
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This article is cited by 6 publications.
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- Anthony J. Hickey, Hugh D. C. Smyth. Computational Modeling of Nonlinear Phenomena Using Machine Learning. 2020, 53-62. https://doi.org/10.1007/978-3-030-42783-2_7
- Rafael Ferreira Dantas, Tereza Cristina Santos Evangelista, Bruno Junior Neves, Mario Roberto Senger, Carolina Horta Andrade, Sabrina Baptista Ferreira, Floriano Paes Silva-Junior. Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings. Expert Opinion on Drug Discovery 2019, 14
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