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Alexander Tropsha
Researcher at University of North Carolina at Chapel Hill
Publications - 306
Citations - 26956
Alexander Tropsha is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Quantitative structure–activity relationship & Virtual screening. The author has an hindex of 71, co-authored 288 publications receiving 22898 citations. Previous affiliations of Alexander Tropsha include Kazan Federal University.
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Beware of q2
TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
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The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
TL;DR: A set of simple guidelines for developing validated and predictive QSPR models is presented, highlighting the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and some algorithms that can be used for this purpose.
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Best Practices for QSAR Model Development, Validation, and Exploitation.
TL;DR: Most critical QSAR modeling routines that are regarded as best practices in the field are examined, including procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries.
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QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more
TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.
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Deep reinforcement learning for de novo drug design
TL;DR: The ReLeaSE method is used to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity.