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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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Journal ArticleDOI
Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches
TL;DR: A novel strategy for rational design of targeted peptide libraries to select a subset of natural amino acids that are most likely to be present in active peptides for the synthesis of library is developed.
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Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces
TL;DR: It is concluded that QSBR models built with the ENTess descriptors can be instrumental for predicting the binding affinity of receptor-ligand complexes.
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A critical overview of computational approaches employed for COVID-19 drug discovery.
Eugene N. Muratov,Rommie E. Amaro,Carolina Horta Andrade,Nathan Brown,Sean Ekins,Denis Fourches,Olexandr Isayev,Dima Kozakov,José L. Medina-Franco,Kenneth M. Merz,Tudor I. Oprea,Tudor I. Oprea,Tudor I. Oprea,Vladimir Poroikov,Gisbert Schneider,Matthew H. Todd,Alexandre Varnek,Alexandre Varnek,David A. Winkler,David A. Winkler,David A. Winkler,Alexey V. Zakharov,Artem Cherkasov,Alexander Tropsha +23 more
TL;DR: In this article, the authors provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature.
Journal ArticleDOI
Quantitative Structure−Activity Relationship Modeling of Dopamine D1 Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods
Brian Hoffman,Sung Jin Cho,Weifan Zheng,Steven D. Wyrick,David E. Nichols,Richard B. Mailman,Alexander Tropsha +6 more
TL;DR: The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D(1) ligands.
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Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity
TL;DR: It is suggested that combining NTP-HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology.