World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
83
Citations
37607
World Ranking
886
National Ranking
483

Chemistry

D-Index
83
Citations
38076
World Ranking
2889
National Ranking
983

Biology and Biochemistry

D-Index
85
Citations
36677
World Ranking
3084
National Ranking
1560

Overview

Alexander Tropsha is affiliated with the University of North Carolina at Chapel Hill in the United States. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology, and Computer Science. The scientist's work includes significant contributions to Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Materials Chemistry, and Artificial Intelligence.

Their research topics focus heavily on computational methods in drug discovery, with broader interests encompassing bioinformatics and genomic networks, biomedical text mining and ontologies, machine learning in materials science, protein structure and dynamics, metabolomics and mass spectrometry studies, as well as analytical chemistry and chromatography.

Alexander Tropsha's recent publications include:

  • "QSAR without borders," 2020, Chemical Society Reviews
  • "The transformational role of GPU computing and deep learning in drug discovery," 2022, Nature Machine Intelligence
  • "Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR," 2023, Nature Reviews Drug Discovery
  • "A critical overview of computational approaches employed for COVID-19 drug discovery," 2021, Chemical Society Reviews
  • "CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity," 2020, Environmental Health Perspectives

Frequent coauthors collaborating with Alexander Tropsha include Eugene Muratov, Vinícius M. Alves, Denis Fourches, Daniel Korn, and Alexey Zakharov.

Publication venues where Alexander Tropsha's work has appeared often include UNC Libraries, OPAL (Open@LaTrobe) (La Trobe University), bioRxiv (Cold Spring Harbor Laboratory), Journal of Chemical Information and Modeling, and Environmental Health Perspectives.

Alexander Tropsha has contributed to book publications, notably with the Institute of Biomedical Chemistry, Moscow, Russia eBooks. One such work is the "Abstracts of XXVII Symposium 'Bioinformatics and Computer-Aided Drug Discovery'," published in 2021.

Best Publications

  • Beware of q2

    Alexander Golbraikh;Alexander Tropsha

  • The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

    Alexander Tropsha;Paola Gramatica;Vijay K. Gombar

  • QSAR Modeling: Where have you been? Where are you going to?

    Artem Cherkasov;Eugene N. Muratov;Eugene N. Muratov;Denis Fourches;Alexandre Varnek

  • Best Practices for QSAR Model Development, Validation, and Exploitation.

    Alexander Tropsha

  • Deep reinforcement learning for de novo drug design

    Mariya Popova;Mariya Popova;Mariya Popova;Olexandr Isayev;Alexander E Tropsha

  • Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

    Alexander Golbraikh;Alexander Tropsha

  • Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

    Denis Fourches;Eugene N. Muratov;Alexander Tropsha

  • Rational selection of training and test sets for the development of validated QSAR models.

    Alexander Golbraikh;Min Shen;Zhiyan Xiao;Yun De Xiao

  • QSAR without borders

    Eugene N. Muratov;Eugene N. Muratov;Jürgen Bajorath;Robert P. Sheridan;Igor V. Tetko

  • Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

    David L J Alexander;Alexander Tropsha;David Alan Winkler

  • Chemical Basis of Interactions Between Engineered Nanoparticles and Biological Systems

    Qingxin Mu;Guibin Jiang;Lingxin Chen;Hongyu Zhou;Hongyu Zhou

  • Universal fragment descriptors for predicting properties of inorganic crystals

    Olexandr Isayev;Corey Oses;Cormac Toher;Eric Gossett

  • Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle

    Weifan Zheng;Alexander Tropsha

  • Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

    Alexander E Tropsha;Alexander Golbraikh

  • Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection

    Igor V. Tetko;Iurii Sushko;Anil Kumar Pandey;Hao Zhu

  • Autoimmunity is triggered by cPR-3(105–201), a protein complementary to human autoantigen proteinase-3

    William F Pendergraft;Gloria A Preston;Ruchir R Shah;Alexander Tropsha

  • Quantitative Nanostructure−Activity Relationship Modeling

    Denis Fourches;Dongqiuye Pu;Carlos Tassa;Ralph Weissleder

  • Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

    Olexandr Isayev;Denis Fourches;Eugene N. Muratov;Corey Oses

  • CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    Kamel Mansouri;Ahmed Abdelaziz;Aleksandra Rybacka;Alessandra Roncaglioni

  • Does rational selection of training and test sets improve the outcome of QSAR modeling

    Todd M. Martin;Paul Harten;Douglas M. Young;Eugene N. Muratov;Eugene N. Muratov

  • Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results.

    Sung Jin Cho;Alexander Tropsha

  • Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

    Hao Zhu;Alexander Tropsha;Denis Fourches;Alexandre Varnek

Frequent Co-Authors

Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Denis Fourches
Denis Fourches North Carolina State University
Hao Zhu
Hao Zhu Rutgers, The State University of New Jersey
Olexandr Isayev
Olexandr Isayev Carnegie Mellon University
Ivan Rusyn
Ivan Rusyn Texas A&M University
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Alexandre Varnek
Alexandre Varnek University of Strasbourg
Stefano Curtarolo
Stefano Curtarolo Duke University
Jan F. Prins
Jan F. Prins University of North Carolina at Chapel Hill
Jack Snoeyink
Jack Snoeyink University of North Carolina at Chapel Hill

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can open new doors for students interested in Computer Science and related fields. A popular option for many is an online bachelors degree, which offers flexibility and affordability for busy learners or those balancing work and study.

For those seeking a more technical focus, pursuing an engineering online degree is an excellent pathway. These programs cover core concepts in software, hardware, and systems design, equipping graduates for diverse tech roles.

Professionals aiming to transition into management or executive roles should consider an executive mba. This degree can enhance leadership skills and broaden opportunities across technology-driven industries.

For those interested in organizing information, digital resources, or working in educational settings, an online mlis may be the right fit. This path prepares students for emerging library and information science careers.

Ultimately, online degree options in the USA offer flexible career pathways for a wide range of interests within and beyond Computer Science.

Best Scientists Citing Alexander Tropsha

Trending Scientists

Recently Published Articles