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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 principles of compound selection for combinatorial library design.
Alexander Tropsha,Weifan Zheng +1 more
TL;DR: Rational approaches to selecting representative subsets of virtual libraries that help direct experimental synthetic efforts for both targeted and diverse library design are discussed.
Journal ArticleDOI
Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets
Jui-Hua Hsieh,Shuangye Yin,Shubin Liu,Alexander Sedykh,Nikolay V. Dokholyan,Alexander Tropsha +5 more
TL;DR: It is found that the combination of QSBAR models and MedusaScore into consensus scoring function affords higher prediction accuracy than any of the contributing methods achieving R(2) values of 0.45/0.58 (Set1/Set2).
Journal ArticleDOI
Learning from history: do not flatten the curve of antiviral research!
Tesia Bobrowski,Cleber C. Melo-Filho,Daniel Korn,Vinicius M. Alves,Konstantin I. Popov,Scott S. Auerbach,Charles Schmitt,Nathaniel J. Moorman,Eugene N. Muratov,Alexander Tropsha +9 more
TL;DR: It is argued that, to develop effective treatments for COVID-19 and be prepared for future epidemics, long-term, consistent investment in antiviral research is needed.
Journal ArticleDOI
Recent progress on cheminformatics approaches to epigenetic drug discovery.
Zoe L Sessions,Norberto Sánchez-Cruz,Fernando D. Prieto-Martínez,Vinicius M. Alves,Hudson P. Santos,Eugene N. Muratov,Alexander Tropsha,José L. Medina-Franco +7 more
TL;DR: The advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles are reviewed, the current chemogenomics data available for epigenetics targets are summarized, and a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery is provided.
Journal ArticleDOI
Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.
Denis Fourches,Eugene N. Muratov,Eugene N. Muratov,Feng Ding,Nikolay V. Dokholyan,Alexander Tropsha +5 more
TL;DR: This study shows that externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and ligand-based QSar models can complement structure- based approaches by boosting the prediction performances when used in consensus.