A. Tropsha

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Mining Spatial Motifs from Protein Structure Graphs

J. Snoeyink, Jun Huan, Wei Wang, 2003 .

Four-body potentials reveal protein-specific correlations to stability changes caused by hydrophobic core mutations.

A. Tropsha, C. Carter, B. Lefebvre, 2001, Journal of molecular biology.

Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions

A. Tropsha, Alexander Sedykh, D. Fourches, 2013, Pharmaceutical Research.

kappa Nearest neighbors QSAR modeling as a variational problem: theory and applications.

A. Tropsha, Peter Itskowitz, 2005, Journal of chemical information and modeling.

Three new consensus QSAR models for the prediction of Ames genotoxicity.

L. Hall, L. Kier, A. Tropsha, 2004, Mutagenesis.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

I. Rusyn, A. Tropsha, Alexander Sedykh, 2010, Environmental health perspectives.

Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

I. Rusyn, A. Tropsha, Alexander Sedykh, 2014, Current topics in medicinal chemistry.

Alarms about Structural Alerts

A. Tropsha, 2016 .

Comprehensive characterization of the Published Kinase Inhibitor Set

John P. Overington, D. J. Price, N. Sepetov, 2016, Nature Biotechnology.

Beware of q2!

A. Tropsha, A. Golbraikh, 2002, Journal of molecular graphics & modelling.

Beware of q 2

A. Tropsha, A. Golbraikh, 2002 .

Distance-based identification of structure motifs in proteins using constrained frequent subgraph mining.

J. Snoeyink, Jun Huan, Wei Wang, 2006, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates.

A. Tropsha, A. Golbraikh, V. Gombar, 2003, Journal of medicinal chemistry.

COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database

Rada Y. Chirkova, Tesia M. Bobrowski, A. Tropsha, 2020, Bioinform..

Systems Chemical Biology

Tudor I. Oprea, T. Oprea, A. Tropsha, 2019, Methods in Molecular Biology.

Quantitative structure-activity relationship modeling of dopamine D(1) antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods.

D. E. Nichols, R. Mailman, A. Tropsha, 1999, Journal of medicinal chemistry.

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

Stephen J. Capuzzi, O. Isayev, A. Tropsha, 2016, Front. Environ. Sci..

Chemical basis of interactions between engineered nanoparticles and biological systems.

Alexander Tropsha, Denis Fourches, Guibin Jiang, 2014, Chemical reviews.

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

A Tropsha, A. Tropsha, S. Cho, 1995, Journal of medicinal chemistry.

Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.

Alexander Golbraikh, Alexander Tropsha, Cécile Béguin, 2004, Journal of medicinal chemistry.

QSAR Modeling of Datasets with Enantioselective Compounds using Chirality Sensitive Molecular Descriptors

A Tropsha, A. Tropsha, S. Oloff, 2005, SAR and QSAR in environmental research.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals

Jessica A. Wignall, I. Rusyn, A. Tropsha, 2018, Environmental health perspectives.

Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.

A. Tropsha, A. Golbraikh, Shuxing Zhang, 2006, Journal of medicinal chemistry.

QSAR modeling: where have you been? Where are you going to?

J. Dearden, R. Todeschini, R. Cramer, 2014, Journal of medicinal chemistry.

Computational systems chemical biology.

Tudor I. Oprea, T. Oprea, A. Tropsha, 2011, Methods in molecular biology.

Do crystal structures obviate the need for theoretical models of GPCRs for structure‐based virtual screening?

A. Tropsha, Hao Tang, J. Hsieh, 2012, Proteins.

Computer-Aided Discovery of New Solubility-Enhancing Drug Delivery System

Vinicius M. Alves, A. Tropsha, E. Muratov, 2020, Biomolecules.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Ivan Rusyn, Alexander Tropsha, Denis Fourches, 2011, Chemical research in toxicology.

Drug Design: QSAR in drug discovery

A. Tropsha, 2010 .

Quantitative Structure−Activity Relationship Analysis of Functionalized Amino Acid Anticonvulsant Agents Using k Nearest Neighbor and Simulated Annealing PLS Methods

Alexander Golbraikh, Alexander Tropsha, Yunde Xiao, 2002 .

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016, Environmental health perspectives.

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action

A Tropsha, A. Tropsha, J. Leszczynski, 2011, SAR and QSAR in environmental research.

Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model.

Ruili Huang, Christopher P Austin, Raymond R Tice, 2012, Toxicological sciences : an official journal of the Society of Toxicology.

Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors.

Paul Shinn, Shihua He, Wei Zheng, 2018, Journal of medicinal chemistry.

QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Alexander Golbraikh, Tudor I. Oprea, Alexander Tropsha, 2008, Pharmaceutical Research.

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

Alexander Tropsha, Hao Zhu, Todd M Martin, 2009, Chemical research in toxicology.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

Ivan Rusyn, Georgia Melagraki, Alexander Tropsha, 2013, Toxicology and applied pharmacology.

Integrative chemical-biological read-across approach for chemical hazard classification.

Alexander Golbraikh, Ivan Rusyn, Alexander Tropsha, 2013, Chemical research in toxicology.

COVID-19 Knowledge Extractor (COKE): a tool and a web portal to extract drug - target protein associations from the CORD-19 corpus of scientific publications on COVID-19

Nancy C. Baker, Rada Y. Chirkova, Vinicius M. Alves, 2020, ChemRxiv : the preprint server for chemistry.

Development of kNN QSAR Models for 3-Arylisoquinoline Antitumor Agents

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2011 .

Rational design of a three‐heptad coiled‐coil protein and comparison by molecular dynamics simulation with the GCN4 coiled coil: Presence of interior three‐center hydrogen bonds

A Tropsha, A. Tropsha, B. W. Erickson, 1994, Protein science : a publication of the Protein Society.

Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles

A. Tropsha, D. Fourches, B. Yan, 2016, Nanotoxicology.

Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.

Alexander Tropsha, Denis Fourches, J. Barnes, 2010, Chemical research in toxicology.

Quantitative nanostructure-activity relationship modeling.

A. Tropsha, R. Weissleder, S. Shaw, 2010, ACS nano.

Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Ivan Rusyn, Alexander Tropsha, Denis Fourches, 2010, Chemical research in toxicology.

Using Graph Indices for the Analysis and Comparison of Chemical Datasets

Alexander Tropsha, Denis Fourches, A. Tropsha, 2013, Molecular informatics.

A Chemocentric Informatics Approach to Drug Discovery: Identification and Experimental Validation of Selective Estrogen Receptor Modulators as ligands of 5-Hydroxytryptamine-6 Receptors and as Potential Cognition Enhancers

Alexander Tropsha, Bryan L Roth, A. Tropsha, 2012 .

Analyzing Protein Structure Using Almost-Delaunay Tetrahedra

J. Snoeyink, Deepak Bandyopadhyay, A. Tropsha, 2003 .

Identification of Sequence-Specific Tertiary Packing Motifs in Protein Structures using Delaunay Tessellation

Alexander Tropsha, Charles W. Carter, Stephen A. Cammer, 2002 .

Compositional preferences in quadruplets of nearest neighbor residues in protein structures: statistical geometry analysis

Iosif I. Vaisman, Alexander Tropsha, Weifan Zheng, 1998, Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174).

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

Olexandr Isayev, Alexander Tropsha, Rene Lopez, 2016 .

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

R. Abagyan, Hualiang Jiang, Kristen K Dang, 2021, PLoS Comput. Biol..

Antitumor Agents. Part 183. Syntheses, Conformational Analyses, and Antitubulin Activity of Allothiocolchicinoids.

A. Tropsha, A. Brossi, A. McPhail, 1998 .

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.

Alexander Tropsha, Denis Fourches, Nicole Kleinstreuer, 2016, Green chemistry : an international journal and green chemistry resource : GC.

HTS navigator: freely accessible cheminformatics software for analyzing high-throughput screening data

Maria F. Sassano, Bryan L. Roth, Alexander Tropsha, 2014, Bioinform..

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance

Alexander Tropsha, Denis Fourches, Eugene Muratov, 2017, American Journal of Pharmaceutical Education.

COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug–Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19

Nancy C. Baker, Rada Y. Chirkova, Vinicius M. Alves, 2021, J. Chem. Inf. Model..

Rational principles of compound selection for combinatorial library design.

Alexander Tropsha, Weifan Zheng, A. Tropsha, 2002, Combinatorial chemistry & high throughput screening.

QSAR/QSPR Revisited

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2018, Chemoinformatics.

ehp CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016 .

Structure-based function inference using protein family-specific data

J. Snoeyink, Jun Huan, Wei Wang, 2006 .

Letter from the Editors

Matthias Rarey, Alexander Tropsha, Kenneth M. Merz, 2015, J. Chem. Inf. Model..

Conserved coronavirus proteins as targets of broad-spectrum antivirals

Tesia M. Bobrowski, A. Tropsha, R. Baric, 2022, Antiviral Research.

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

A. Tropsha, Alexander Sedykh, D. Fourches, 2018, Drug Safety.

A Systems Biology Workflow for Drug and Vaccine Repurposing: Identifying Small-Molecule BCG Mimics to Reduce or Prevent COVID-19 Mortality

A. Tropsha, R. Hajjo, 2020, Pharmaceutical Research.

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

Tesia M. Bobrowski, A. Tropsha, A. Zakharov, 2022, ACS Pharmacology & Translational Science.

“ Everything Out ” Validation Approach for Qsar Models of Chemical Mixtures

A. Tropsha, D. Fourches, E. Muratov, 2014 .

Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways

Rada Y. Chirkova, A. Tropsha, D. Korn, 2022, SIGMOD Conference.

Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing

Stephen J. Capuzzi, A. Tropsha, A. Jadhav, 2017, Oncotarget.

The Rare Disease InfoHub Portal: Actionable Knowledge Appropriately Delivered

Rada Y. Chirkova, A. Tropsha, D. Korn, 2021 .

Molecular cloning and characterization of an invertebrate cellular retinoic acid binding protein.

A. Tropsha, S. Cammer, S. Mansfield, 1998, Proceedings of the National Academy of Sciences of the United States of America.

Molecular Drug Design and Dopamine Receptors

R. Mailman, A. Tropsha, D. Nichols, 1997 .

Recent progress on cheminformatics approaches to epigenetic drug discovery.

Vinicius M. Alves, J. Medina-Franco, A. Tropsha, 2020, Drug discovery today.

QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2

Vinicius M. Alves, Tesia M. Bobrowski, A. Tropsha, 2020, Molecular informatics.

Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects

A. Tropsha, R. Hajjo, D. Sabbah, 2022, Pathogens.

Generating folded protein structures with a lattice chain growth algorithm

T. Schlick, A. Tropsha, H. H. Gan, 2000 .

The effect of region size on CoMFA analyses

Alexander Tropsha, A. Tropsha, Ehren C. Bucholtz, 1999 .

QSAR Modeling and Prediction of Drug-Drug Interactions.

Marc C. Nicklaus, Alexander Tropsha, Denis Fourches, 2016, Molecular pharmaceutics.

Generalized linear response method: Application to hydration free energy calculations

Xin Chen, Alexander Tropsha, A. Tropsha, 1999, J. Comput. Chem..

Applications of machine learning and artificial intelligence to designing chemicals and materials with the desired properties

A. Tropsha, 2018 .

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design

Boris Ginsburg, O. Isayev, A. Tropsha, 2020, J. Chem. Inf. Model..

Repurposing Quaternary Ammonium Compounds as Potential Treatments for COVID-19

Nancy C. Baker, Antony J. Williams, A. Tropsha, 2020, Pharmaceutical Research.

Shedding the Light on Post-Vaccine Myocarditis and Pericarditis in COVID-19 and Non-COVID-19 Vaccine Recipients

A. Tropsha, R. Hajjo, S. Bardaweel, 2021, Vaccines.

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

Stephen J. Capuzzi, T. Willson, O. Isayev, 2022, Communications Chemistry.

Integrated approach to elucidate metal-implant related adverse outcome pathways.

Vinicius M. Alves, A. Tropsha, E. Muratov, 2022, Regulatory toxicology and pharmacology : RTP.

HIV‐1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

A. Tropsha, A. Kaplan, Shuxing Zhang, 2008, Proteins.

Synthesis and structure activity relationships of DCLK1 kinase inhibitors based on a 5,11-dihydro-6H-benzo[e]pyrimido[5,4-b][1,4]diazepin-6-one scaffold.

Stephen J. Capuzzi, A. Tropsha, N. Gray, 2020, Journal of Medicinal Chemistry.

Evaluation of the relative stability of liganded versus ligand‐free protein conformations using Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method

A. Tropsha, J. B. Pitner, Shuxing Zhang, 2004, Proteins.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity

Vinicius M. Alves, Joyce V. B. Borba, A. Tropsha, 2020, Environmental health perspectives.

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

Tesia M. Bobrowski, A. Tropsha, A. Zakharov, 2022, ACS pharmacology & translational science.

Multi-Descriptor Read Across (MuDRA): a simple and transparent approach for developing accurate QSAR models

Wai In Lam, Vinicius M. Alves, Stephen J. Capuzzi, 2021 .

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules

A. Tropsha, A. Zakharov, A. Simeonov, 2022, bioRxiv.

Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

Alexander Golbraikh, Bryan L. Roth, Alexander Tropsha, 2014, J. Chem. Inf. Model..

Recent trends in statistical QSAR modeling of environmental chemical toxicity.

Alexander Tropsha, A. Tropsha, 2012, Experientia supplementum.

Recent Advances in Development, Validation, and Exploitation of QSAR Models

Alexander Tropsha, A. Tropsha, 2010, Burger's Medicinal Chemistry and Drug Discovery.

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

Alexander Tropsha, Denis Fourches, Eugene N. Muratov, 2010, J. Chem. Inf. Model..

Best Practices for QSAR Model Development, Validation, and Exploitation

Alexander Tropsha, A. Tropsha, 2010, Molecular informatics.

Free online resources enabling crowd-sourced drug discovery

Antony J. Williams, Sean Ekins, Alexander Tropsha, 2009 .

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

Alexander Golbraikh, Alexander Tropsha, Denise G. Teotico, 2008, J. Comput. Aided Mol. Des..

Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

Alexander Golbraikh, Hao Zhu, Douglas M. Young, 2012, J. Chem. Inf. Model..

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

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2004, Molecular Diversity.

Chembench: a cheminformatics workbench

Alexander Tropsha, Christopher M. Grulke, Diane Pozefsky, 2010, Bioinform..

Efficient Design of Peptide-Binding Polymers Using Active Learning Approaches

A. Tropsha, T. Madzhidov, A. Rakhimbekova, 2021, bioRxiv.

Systems chemical biology.

Tudor I. Oprea, Alexander Tropsha, Mark Daniel Rintoul, 2007 .

Modelling the auxin-binding site of auxin-binding protein 1 of maize

Alexander Tropsha, Alan M. Jones, A. Tropsha, 1994 .

Computational models identify several FDA approved or experimental drugs as putative agents against SARS-CoV-2

Vinicius M. Alves, Tesia M. Bobrowski, A. Tropsha, 2020, ChemRxiv : the preprint server for chemistry.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

A. Tropsha, R. Hajjo, S. Bardaweel, 2021, Diagnostics.

Pred-Skin: A web portal for accurate prediction of human skin sensitizers.

Joyce V. B. Borba, Vinicius M. Alves, A. Tropsha, 2020, Chemical research in toxicology.

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development

Wei Wang, Jack Snoeyink, Jun Huan, 2009, J. Comput. Aided Mol. Des..

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

A. Tropsha, C. Carter, Ruchir R. Shah, 2004, Nature Medicine.

Novel ligands for the human histamine H1 receptor: synthesis, pharmacology, and comparative molecular field analysis studies of 2-dimethylamino-5-(6)-phenyl-1,2,3,4-tetrahydronaphthalenes.

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2006, Bioorganic & medicinal chemistry.

Clozapine-induced agranulocytosis is associated with rare HLA-DQB1 and HLA-B alleles

M. Daly, D. Rujescu, K. Shianna, 2014, Nature Communications.

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

Paola Gramatica, Alexander Tropsha, Vijay K. Gombar, 2003 .

Surely you are joking, Mr Docking!

Tudor I. Oprea, T. Oprea, A. Tropsha, 2023, Chemical Society reviews.

A critical overview of computational approaches employed for COVID-19 drug discovery

Tudor I. Oprea, Rommie E. Amaro, G. Schneider, 2021, Chemical Society reviews.

An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters.

A. Tropsha, Alexander Sedykh, K. Brouwer, 2013, Journal of pharmaceutical sciences.

Pred‐hERG: A Novel web‐Accessible Computational Tool for Predicting Cardiac Toxicity

Alexander Tropsha, Denis Fourches, Eugene Muratov, 2015, Molecular informatics.

Tuning HERG out: antitarget QSAR models for drug development.

Alexander Tropsha, Denis Fourches, Eugene Muratov, 2014, Current topics in medicinal chemistry.

The transformational role of GPU computing and deep learning in drug discovery

O. Isayev, A. Tropsha, A. Cherkasov, 2022, Nature Machine Intelligence.

Making sense from antisense: A review of experimental data and developing ideas on sense–antisense peptide recognition

A Tropsha, A. Tropsha, I. Chaiken, 1992, Journal of molecular recognition : JMR.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

Kristen K. Dang, Robert J. Allaway, R. Abagyan, 2021, bioRxiv.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Jerzy Leszczynski, Haralambos Sarimveis, Philip Doganis, 2020, Computational and structural biotechnology journal.

Universal fragment descriptors for predicting properties of inorganic crystals

Cormac Toher, Corey Oses, Stefano Curtarolo, 2016, Nature Communications.

Materials Informatics

Alexander Tropsha, Hanoch Senderowitz, A. Tropsha, 2019, J. Chem. Inf. Model..

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

Igor V. Tetko, Hao Zhu, Alexandre Varnek, 2008, J. Chem. Inf. Model..

Mining protein family specific residue packing patterns from protein structure graphs

Wei Wang, Jack Snoeyink, Jun Huan, 2004, RECOMB.

PITPs as Targets for Selectively Interfering With Phosphoinositide Signaling in Cells

Ronald W. Davis, G. Giaever, A. Tropsha, 2013, Nature chemical biology.

Deep reinforcement learning for de novo drug design

Olexandr Isayev, Alexander Tropsha, Mariya Popova, 2017, Science Advances.

CATMoS: Collaborative Acute Toxicity Modeling Suite

Kimberley M. Zorn, Daniel P. Russo, Timothy E. H. Allen, 2021, Environmental health perspectives.

An atypical heterotrimeric Gα protein has substantially reduced nucleotide binding but retains nucleotide-independent interactions with its cognate RGS protein and Gβγ dimer

Alan M. Jones, A. Tropsha, Tigran M. Abramyan, 2019, Journal of biomolecular structure & dynamics.

A decoy heterotrimeric Gα protein has substantially reduced nucleotide binding but retains nucleotide-independent interactions with its cognate RGS protein and Gβγ dimer

Alan M. Jones, A. Tropsha, Tigran M. Abramyan, 2019 .

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.

Alexander Tropsha, Denis Fourches, Nicole Kleinstreuer, 2015, Toxicology and applied pharmacology.

Delaunay Tessellation of Proteins: Four Body Nearest-Neighbor Propensities of Amino Acid Residues

Iosif I. Vaisman, Alexander Tropsha, Raj K. Singh, 1996, J. Comput. Biol..

Lattice protein folding with two and four‐body statistical potentials

T Schlick, A Tropsha, T. Schlick, 2001, Proteins.

QSAR without borders.

Artem Cherkasov, Tudor I. Oprea, Jürgen Bajorath, 2020, Chemical Society reviews.

Statistical geometry analysis of proteins: implications for inverted structure prediction.

A Tropsha, A. Tropsha, W. Zheng, 1996, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth

Tudor I. Oprea, Alexander Tropsha, Andrei Leitão, 2013, Comput. Biol. Chem..

Recent Trends in Quantitative Structure‐Activity Relationships

Alexander Tropsha, A. Tropsha, 2003 .

Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships

Alexander Tropsha, Denis Fourches, Ivan Rusyn, 2018, J. Chem. Inf. Model..

HIV Pharmacology Data Repository (PDR): Setting the New Information Sharing Standard for Pharmacokinetic Studies

A. Tropsha, R. Hubal, R. Hubal, 2023, bioRxiv.

Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

Igor V. Tetko, Artem Cherkasov, Klaus-Robert Müller, 2010, J. Chem. Inf. Model..

Community-wide assessment of protein-interface modeling suggests improvements to design methodology.

Kengo Kinoshita, Haruki Nakamura, Genki Terashi, 2011, Journal of molecular biology.

Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F

Alexander Golbraikh, Alexander Tropsha, Hao Tang, 2008, J. Chem. Inf. Model..

QSAR modeling of GPCR ligands: methodologies and examples of applications.

A Tropsha, S X Wang, A. Tropsha, 2006, Ernst Schering Foundation symposium proceedings.

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

Alexander Tropsha, Eugene N. Muratov, Stephen J. Capuzzi, 2017, J. Chem. Inf. Model..

A bibliometric review of drug repurposing.

Sean Ekins, Nancy C Baker, Alexander Tropsha, 2018, Drug discovery today.

Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise

Nikolay V. Dokholyan, Konstantin Popov, Alexander Tropsha, 2016, J. Chem. Inf. Model..

Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs

Alexander Tropsha, Alexander Kabanov, Eugene Muratov, 2019, Science Advances.

Using semantic analysis of texts for the identification of drugs with similar therapeutic effects

Sergey I. Nikolenko, Elena Tutubalina, Timur I. Madzhidov, 2017, Russian Chemical Bulletin.

Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method.

Alexander Golbraikh, Alexander Tropsha, Zhiyan Xiao, 2002, Journal of medicinal chemistry.

Reproducibility, sharing and progress in nanomaterial databases.

Anthony J. Hickey, Alexander Tropsha, Karmann C. Mills, 2017, Nature nanotechnology.

Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models

Alexander Golbraikh, Alexander Tropsha, Diane Pozefsky, 2018, J. Chem. Inf. Model..

Computational Methods for Drug Discovery and Design.

A. Tropsha, J. Bajorath, 2016, Journal of Medicinal Chemistry.

Semantics-driven frequent data pattern mining on electronic health records for effective adverse drug event monitoring

He Zhang, Jun Huan, Alexander Tropsha, 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

Laura Christopherson, Alexander Tropsha, Xialan Dong, 2020, J. Biomed. Informatics.

Toxicokinetic Triage for Environmental Chemicals.

Imran Shah, Sieto Bosgra, Barbara A Wetmore, 2015, Toxicological sciences : an official journal of the Society of Toxicology.

The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

A. Tropsha, Alexander Sedykh, Shu-Shen Liu, 2013, Pharmaceutical Research.

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

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2007, Current pharmaceutical design.

Structure‐based function inference using protein family‐specific fingerprints

Jinze Liu, Jack Snoeyink, Jun Huan, 2006, Protein science : a publication of the Protein Society.

Recent trends in computer-aided drug discovery.

A Tropsha, A. Tropsha, 2000, Current opinion in drug discovery & development.

An Efficient Projection Protocol for Chemical Databases: Singular Value Decomposition Combined with Truncated-Newton Minimization

Tamar Schlick, Alexander Tropsha, Dexuan Xie, 2000, J. Chem. Inf. Comput. Sci..

Automated Pharmacophore Identification for Large Chemical Data Sets1

Xin Chen, Alexander Tropsha, S. Stanley Young, 1999, J. Chem. Inf. Comput. Sci..

Accurate prediction of the bound conformation of galanthamine in the active site of Torpedo californica acetylcholinesterase using molecular docking.

A Tropsha, D Lamba, A. Tropsha, 2001, Journal of molecular graphics & modelling.

Cross-Validated R2 Guided Region Selection for CoMFA Studies

Sung Jin Cho, Alexander Tropsha, A. Tropsha, 1998 .

Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents

Tesia M. Bobrowski, A. Tropsha, A. Zakharov, 2022, bioRxiv.

Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations

Alexander Tropsha, Bala Krishnamoorthy, A. Tropsha, 2003, Bioinform..

Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract.

Alexander Tropsha, Denis Fourches, Eugene Muratov, 2014, AIDS research and human retroviruses.

Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides

Alexander Tropsha, Olexandr Isayev, Sherif Farag, 2019, Bioinform..

Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods.

Alexander Tropsha, Zhiyan Xiao, Yun-De Xiao, 2004, Journal of molecular graphics & modelling.

Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs

Wei Wang, Jack Snoeyink, Jun Huan, 2005, J. Comput. Biol..

Accurate Classification of Protein Structural Families Using Coherent Subgraph Analysis

Wei Wang, Jun Huan, Alexander Tropsha, 2003, Pacific Symposium on Biocomputing.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Ruili Huang, Scott Boyer, Xin Hu, 2020, Environmental health perspectives.

The Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure Activity Relationship Models of Animal Carcinogenicity

Ivan Rusyn, Hao Zhu, Ann Richard, 2007 .

Knowledge-based approaches to drug discovery for rare diseases.

Nancy C. Baker, Rada Y. Chirkova, Stephen J. Capuzzi, 2021, Drug discovery today.

Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods.

Ivan Rusyn, Alexander Tropsha, Regina Politi, 2014, Toxicology and applied pharmacology.

Curated Data In — Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing

Vinicius M. Alves, I. Rusyn, A. Tropsha, 2021, Alternatives to laboratory animals : ATLA.

New drug candidates for liposomal delivery identified by computer modeling of liposomes' remote loading and leakage

Alexander Tropsha, David Marcus, Amiram Goldblum, 2017, Journal of controlled release : official journal of the Controlled Release Society.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Igor V. Tetko, Hao Zhu, Artem Cherkasov, 2008, J. Chem. Inf. Model..

Quantitative Structure–activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

Alexander Golbraikh, Alexander Tropsha, José L. Medina-Franco, 2005, J. Comput. Aided Mol. Des..

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications

Wei Wang, Jack Snoeyink, Jun Huan, 2009, J. Comput. Aided Mol. Des..

Functional Neighbors: Inferring Relationships between Non-Homologous Protein Families Using Family-Specific Packing Motifs

Wei Wang, Jack Snoeyink, Jinze Liu, 2008, 2008 IEEE International Conference on Bioinformatics and Biomedicine.

Rapid Determination of Local Structural Features Common to a Set of Proteins

J. Snoeyink, Jun Huan, Wei Wang, 2005 .

Alarms about structural alerts.

Vinicius M. Alves, Denis Fourches, Alexander Tropsha, 2016, Green chemistry : an international journal and green chemistry resource : GC.

Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle

Alexander Tropsha, Weifan Zheng, A. Tropsha, 2000, J. Chem. Inf. Comput. Sci..

Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Ivan Rusyn, Alexander Tropsha, Kathryn Z Guyton, 2012, Toxicological sciences : an official journal of the Society of Toxicology.

Predictive quantitative structure-activity relationship modeling

A. Tropsha, A. Tropsha, 2007 .

Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening

Julie Clark, Alexander Golbraikh, Hao Zhu, 2013, J. Chem. Inf. Model..

Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions

A. Tropsha, Alexander Sedykh, D. Fourches, 2013, Pharmaceutical Research.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

Alexander Tropsha, Marc Parham, Scott Oloff, 2006, Journal of medicinal chemistry.

Combinatorial QSAR of Ambergris Fragrance Compounds

Alexander Golbraikh, Alexander Tropsha, Weifan Zheng, 2004, J. Chem. Inf. Model..

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

Alexander Golbraikh, Min Shen, Alexander Tropsha, 2003, J. Comput. Aided Mol. Des..

Data Set Modelability by QSAR

Alexander Golbraikh, Alexander Tropsha, Denis Fourches, 2014, J. Chem. Inf. Model..

Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs.

Alexander Golbraikh, Alexander Tropsha, Christopher M Grulke, 2010, Journal of medicinal chemistry.

Calculation of the Relative Binding Affinity of Enzyme Inhibitors Using the Generalized Linear Response Method.

Alexander Tropsha, A. Tropsha, Xin Chen, 2006, Journal of chemical theory and computation.

Expanding the scope of drug repurposing in pediatrics: the Children's Pharmacy Collaborative.

Alexander Tropsha, Denis Fourches, Sherif Farag, 2014, Drug discovery today.

Defining clinical outcome pathways.

Stephen W. Edwards, Rada Y. Chirkova, Stephen J. Capuzzi, 2022, Drug discovery today.

Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches

Sung Jin Cho, Alexander Tropsha, Weifan Zheng, 1998, J. Chem. Inf. Comput. Sci..

The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

Shu-Shen Liu, Alexander Tropsha, Xiang-Wei Zhu, 2013, Pharmaceutical Research.

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

David A. Winkler, A. Tropsha, D. L. J. Alexander, 2015, J. Chem. Inf. Model..

A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models

Alexander Golbraikh, Alexander Tropsha, Scott Oloff, 2006, J. Chem. Inf. Model..

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

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2002, J. Comput. Aided Mol. Des..

Editorial: Method and Data Sharing and Reproducibility of Scientific Results

Rommie E. Amaro, A. Tropsha, M. Rarey, 2020, J. Chem. Inf. Model..

The Development of Novel Chemical Fragment‐Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling

Alexander Tropsha, Weifan Zheng, Raed Khashan, 2014, Molecular informatics.

Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles.

Alexander Tropsha, Denis Fourches, Dongqiuye Pu, 2011, Combinatorial chemistry & high throughput screening.

Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses

Alexander Tropsha, Xialan Dong, Changsung Moon, 2021, J. Biomed. Informatics.

PreSS/MD: Predictor of Skin Sensitization Caused by Chemicals Leaching from Medical Devices

Vinicius M. Alves, Joyce V. B. Borba, A. Tropsha, 2022 .

Novel Classification of Mono-Molecular Odorants using Standardized Semantic Profiles

Vinicius M. Alves, A. Tropsha, E. Muratov, 2022 .

Correction: QSAR without borders.

Tudor I. Oprea, I. Tetko, D. Agrafiotis, 2020, Chemical Society reviews.

Structure-based alignment and comparative molecular field analysis of acetylcholinesterase inhibitors.

A Tropsha, A. Tropsha, S. Cho, 1996, Journal of medicinal chemistry.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Alexey V Zakharov, Adam Yasgar, Alexander Tropsha, 2020, J. Chem. Inf. Model..

A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery

Stephen J. Capuzzi, T. Willson, O. Isayev, 2021 .

Curation of chemogenomics data.

Alexander Tropsha, Denis Fourches, Eugene Muratov, 2015, Nature chemical biology.

TEXT MINING OF THE PEOPLE's PHARMACY RADIO SHOW TRANSCRIPTS CAN IDENTIFY NOVEL DRUG REPURPOSING HYPOTHESES

A. Tropsha, E. Muratov, D. Korn, 2022, medRxiv.

A motif found in propeptides and prohormones that may target them to secretory vesicles.

A. Tropsha, J. Kizer, 1991, Biochemical and biophysical research communications.

Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation

Alexander Tropsha, Denis Fourches, Eugene N. Muratov, 2016, J. Chem. Inf. Model..

Molecular simulation of alkyl boronic acids: Molecular mechanics and solvation free energy calculations

Alexander Tropsha, Xiannong Chen, Libero Bartolotti, 1994, J. Comput. Chem..

QSAR Modeling Using Chirality Descriptors Derived from Molecular Topology

Alexander Golbraikh, Alexander Tropsha, A. Tropsha, 2003, J. Chem. Inf. Comput. Sci..

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals

Alexander Tropsha, Judy Strickland, Eugene N. Muratov, 2017, J. Chem. Inf. Model..

Discrete Molecular Dynamics Distinguishes Nativelike Binding Poses from Decoys in Difficult Targets

Alexander Tropsha, Shuangye Yin, Nikolay V Dokholyan, 2022 .

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Cormac Toher, Corey Oses, Stefano Curtarolo, 2017, Computational Materials Science.

ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources

Karamarie Fecho, Alexander Tropsha, Kenneth Morton, 2019, J. Chem. Inf. Model..

Simplicial neighborhood analysis of protein packing (SNAPP): a computational geometry approach to studying proteins.

Alexander Tropsha, Iosif I Vaisman, Charles W Carter, 2003, Methods in enzymology.

Chembench: A Publicly Accessible, Integrated Cheminformatics Portal

Alexander Tropsha, Diane Pozefsky, Eugene N. Muratov, 2017, J. Chem. Inf. Model..

Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Alexander Tropsha, Vinicius M Alves, Eugene N Muratov, 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals

Joyce V. B. Borba, Vinicius M. Alves, A. Tropsha, 2021, Artificial intelligence in the life sciences.

PreS/MD: Predictor of Sensitization hazard for chemical substances released from Medical Devices.

Vinicius M. Alves, Joyce V. B. Borba, A. Tropsha, 2022, Toxicological sciences : an official journal of the Society of Toxicology.

A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment

Alexander Tropsha, Thomas Luechtefeld, Eugene N. Muratov, 2018 .

Rational Combinatorial Library Design. 1. Focus-2D: A New Approach to the Design of Targeted Combinatorial Chemical Libraries

Sung Jin Cho, Alexander Tropsha, Weifan Zheng, 1998, J. Chem. Inf. Comput. Sci..

Modernization of Enoxaparin Molecular Weight Determination Using Homogeneous Standards

Stephen J. Capuzzi, A. Tropsha, E. Muratov, 2017, Pharmaceuticals.

Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark

Alexander Tropsha, Denis Fourches, Regina Politi, 2015, J. Chem. Inf. Model..

Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge-Based Pose Scoring and Physical Force Field-Based Hit Scoring Functions Improves the Accuracy of Structure-Based Virtual Screening

Nikolay V. Dokholyan, Alexander Tropsha, Shubin Liu, 2012, J. Chem. Inf. Model..

Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents

Alexander Tropsha, Weifan Zheng, Shuxing Zhang, 2007, J. Comput. Aided Mol. Des..

Novel ZE-Isomerism Descriptors Derived from Molecular Topology and Their Application to QSAR Analysis

Alexander Golbraikh, Danail Bonchev, Alexander Tropsha, 2002, J. Chem. Inf. Comput. Sci..

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

Alexander Tropsha, Jerry O Ebalunode, Weifan Zheng, 2011, Methods in molecular biology.

Chapter 7 Variable Selection QSAR Modeling, Model Validation, and Virtual Screening

Alexander Tropsha, A. Tropsha, 2006 .

Novel Chirality Descriptors Derived from Molecular Topology

Alexander Golbraikh, Danail Bonchev, Alexander Tropsha, 2001, J. Chem. Inf. Comput. Sci..

"New tricks for an old dog": Development and application of novel QSAR methods for rational design of combinatorial chemical libraries and database mining

S. J. Cho, A. Tropsha, W. Zheng, 1999 .

Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation.

Alexander Tropsha, Yuri K Peterson, A. Tropsha, 2009, Journal of medicinal chemistry.

Novel Chirality Descriptors Derived from Molecular Topology.

Alexander Golbraikh, Alexander Tropsha, Danail Bonchev, 2001 .

Distributed Chemical Computing Using ChemStar: An Open Source Java Remote Method Invocation Architecture Applied to Large Scale Molecular Data from PubChem

Muthukumarasamy Karthikeyan, Andreas Bender, Subramanian Krishnan, 2008, J. Chem. Inf. Model..

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Anton Simeonov, Sankalp Jain, Alexey V Zakharov, 2021, J. Chem. Inf. Model..

Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading.

Amiram Goldblum, Alexander Tropsha, Yechezkel Barenholz, 2014, Journal of controlled release : official journal of the Controlled Release Society.

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

Alexander Golbraikh, Amiram Goldblum, Alexander Tropsha, 2012, Journal of controlled release : official journal of the Controlled Release Society.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Ivan Rusyn, G. Niklas Norén, I. Ralph Edwards, 2015, J. Am. Medical Informatics Assoc..

HIV-1 Protease Function and Structure Studies with the Simplicial Neighborhood Analysis of Protein Packing (SNAPP) Method

A. Tropsha, A. Kaplan, Shuxing Zhang, 2009 .

COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database

Rada Chirkova, Alexander Tropsha, Patrick Wang, 2020, Bioinformatics.

Learning from history: do not flatten the curve of antiviral research!

Alexander Tropsha, Charles Schmitt, Eugene N. Muratov, 2020, Drug Discovery Today.

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Alexander Tropsha, Denis Fourches, Nicole Kleinstreuer, 2015, Toxicology and applied pharmacology.

Computational Tools for ADMET Profiling

Antony J. Williams, Imran Shah, Denis Fourches, 2018 .

Predictive QSAR modeling: Methods and applications in drug discovery and chemical risk assessment

Alexander Golbraikh, Hao Zhu, Alexander Tropsha, 2012 .

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface

Karamarie Fecho, Stanley C Ahalt, Hao Xu, 2020, JMIR medical informatics.

A novel approach for exposing and sharing clinical data: the Translator Integrated Clinical and Environmental Exposures Service

Karamarie Fecho, Stanley C Ahalt, James Champion, 2019, J. Am. Medical Informatics Assoc..

Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects

Artem Cherkasov, Alexander Tropsha, Fuqiang Ban, 2019, J. Chem. Inf. Model..

Combinatorial QSAR Modeling of Chemical Toxicants Tested Against Tetrahymena pyriformis.

Igor V. Tetko, Artem Cherkasov, Paola Gramatica, 2008 .

A Turning Point For Blood–Brain Barrier Modeling

Sean Ekins, Alexander Tropsha, A. Tropsha, 2009, Pharmaceutical Research.

Identification of the descriptor pharmacophores using variable selection QSAR: applications to database mining.

A Tropsha, W Zheng, A. Tropsha, 2001, Current pharmaceutical design.

The AFLOW Fleet for Materials Discovery

Robert M. Hanson, Marco Buongiorno Nardelli, Cormac Toher, 2017, Handbook of Materials Modeling.

Molecular simulations of beta-sheet twisting.

A. Tropsha, J. Hermans, L. Wang, 1996, Journal of molecular biology.

Energetic decomposition of the α-helix-coil equilibrium of a dynamic model system

Alexander Tropsha, Lu Wang, Jan Hermans, 1998 .

Comparative Analysis of QSAR‐based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity

Alexander Tropsha, Man Luo, Xiang S Wang, 2016, Molecular informatics.

Computer Aided Drug Design

Alexander Tropsha, Weifan Zheng, A. Tropsha, 2001 .

Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity

Nancy C Baker, Alexander Tropsha, Denis Fourches, 2015, Molecular informatics.

Chemoinformatics approaches to virtual screening

Alexandre Varnek, A. Tropsha, A. Varnek, 2008 .

Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues

Alexander Tropsha, Weifan Zheng, Raed Khashan, 2012, Proteins.

Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods.

Alexander Tropsha, Yunde Xiao, A. Tropsha, 2004, Journal of pharmaceutical sciences.

Rational Combinatorial Library Design. 3. Simulated Annealing Guided Evaluation (SAGE) of Molecular Diversity: A Novel Computational Tool for Universal Library Design and Database Mining

Chris L. Waller, Sung Jin Cho, Alexander Tropsha, 1999, J. Chem. Inf. Comput. Sci..

A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application

Karamarie Fecho, Alexander Tropsha, Chris Bizon, 2020, JMIR medical informatics.

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

Alexander Golbraikh, Fred A. Wright, Ivan Rusyn, 2009, Environmental health perspectives.

Antitumor agents. 199. Three-dimensional quantitative structure-activity relationship study of the colchicine binding site ligands using comparative molecular field analysis.

A. Tropsha, A. Brossi, S. Kuo, 2000, Journal of medicinal chemistry.

Materials Informatics

Alexander Tropsha, Hanoch Senderowitz, A. Tropsha, 2018, J. Chem. Inf. Model..

Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Stephen P. Boyd, Kimberley M. Zorn, Daniel P. Russo, 2021, Environmental health perspectives.

ZINC Express: A Virtual Assistant for Purchasing Compounds Annotated in the ZINC Database

Alexander Tropsha, Eugene N. Muratov, Daniel Robert Korn, 2021, J. Chem. Inf. Model..

The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation

A. Tropsha, E. Muratov, C. Melo-Filho, 2023, 2308.06347.

Thermodynamic parameters for the helix-coil transition of oligopeptides: molecular dynamics simulation with the peptide growth method.

A. Tropsha, J. Hermans, L. Wang, 1995, Proceedings of the National Academy of Sciences of the United States of America.

Why Academic Drug Discovery Makes Sense

Alexander Tropsha, Bryan Roth, A. Tropsha, 2006, Science.

Application of validated QSAR models of D1 dopaminergic antagonists for database mining.

Alexander Tropsha, Scott Oloff, R. Mailman, 2005, Journal of medicinal chemistry.

Genome-wide common and rare variant analysis provides novel insights into clozapine-associated neutropenia

H. Stefánsson, D. Rujescu, A. Jonasdottir, 2016, Molecular Psychiatry.

Genome-wide common and rare variant analysis provides novel insights into clozapine-associated neutropenia

M. Daly, H. Stefánsson, D. Rujescu, 2016, Molecular Psychiatry.

Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets pdf

Rada Chirkova, Alexander Tropsha, Rahul Yedida, 2020, ArXiv.

Conformational analysis of D1 dopamine receptor agonists: pharmacophore assessment and receptor mapping.

D E Nichols, R. Mailman, A. Tropsha, 1996, Journal of medicinal chemistry.

Application of free energy simulations to the binding of a transition-state-analogue inhibitor to HIV protease.

A Tropsha, A. Tropsha, J. Hermans, 1992, Protein engineering.

Novel Inhibitors of Human Histone Deacetylase (HDAC) Identified by QSAR Modeling of Known Inhibitors, Virtual Screening, and Experimental Validation

Hao Tang, Xi-Ping Huang, Bryan L. Roth, 2009, J. Chem. Inf. Model..

The “random‐coil” state of proteins: Comparison of database statistics and molecular simulations

A. Tropsha, J. Hermans, Lu Wang, 1999, Proteins.

A new approach to protein fold recognition based on Delaunay tessellation of protein structure.

A Tropsha, A. Tropsha, S. Cho, 1997, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

Nancy C. Baker, A. Tropsha, R. Hajjo, 2023, NPJ vaccines.

Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

Igor V Tetko, Alexander Tropsha, Uko Maran, 2017, Molecular informatics.

Diversity and Coverage of Structural Sublibraries Selected Using the SAGE and SCA Algorithms

Alexander Tropsha, Weifan Zheng, Lori B. Pfahler, 2001, J. Chem. Inf. Comput. Sci..

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking

A. Tropsha, D. Koes, Konstantin I. Popov, 2023, ArXiv.

A conformational change in heparan sulfate 3-O-sulfotransferase-1 is induced by binding to heparan sulfate.

A. Tropsha, Ruchir R. Shah, S. Edavettal, 2004, Biochemistry.

Target, chemical and bioactivity databases – integration is key

Tudor I. Oprea, Alexander Tropsha, A. Tropsha, 2006 .

Activity prediction and identification of mis‐annotated chemical compounds using extreme descriptors

James Stephen Marron, Jan Hannig, Denis Fourches, 2016 .

Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

A. Tropsha, D. Young, T. Martin, 2012, J. Chem. Inf. Model..

Data Set Modelability by QSAR

A. Tropsha, D. Fourches, E. Muratov, 2014, J. Chem. Inf. Model..

Dataset Modelability by QSAR: Continuous Response Variable

Vinicius M. Alves, A. Tropsha, E. Muratov, 2021, Practical Aspects of Computational Chemistry V.

Modelability Criteria: Statistical Characteristics Estimating Feasibility to Build Predictive QSAR Models for a Dataset

Inta Liepina, Alexander Golbraikh, Alexander Tropsha, 2014 .

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

I. Tetko, A. Tropsha, Phuong Dao, 2008, J. Chem. Inf. Model..

LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING.

Yufeng Liu, Alexander Tropsha, J S Marron, 2012, The annals of applied statistics.

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

A. Tropsha, D. Winkler, D. Alexander, 2015, J. Chem. Inf. Model..

Integrative chemical-biological read-across approach for chemical hazard classification.

I. Rusyn, A. Tropsha, Alexander Sedykh, 2013, Chemical research in toxicology.

Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening

A. Tropsha, S. Ekins, Alexander Sedykh, 2013, J. Chem. Inf. Model..

Combinatorial QSAR Modeling of P-Glycoprotein Substrates

Alexander Golbraikh, Alexander Tropsha, Yunde Xiao, 2006, J. Chem. Inf. Model..

Chemical basis of interactions between engineered nanoparticles and biological systems.

A. Tropsha, G. Jiang, D. Fourches, 2014, Chemical reviews.

A topological characterization of protein structure

Alexander Tropsha, Bala Krishnamoorthy, Scott Provan, 2007 .

Lattice protein folding with two and four‐body statistical potentials

T. Schlick, A. Tropsha, H. H. Gan, 2001, Proteins.

Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks?

Vinicius M. Alves, Tesia M. Bobrowski, A. Tropsha, 2023, FEMS microbiology reviews.

Do interhelical side chain-backbone hydrogen bonds participate in formation of leucine zipper coiled coils?

A. Tropsha, F. Brown, J. Bowen, 1991, Proceedings of the National Academy of Sciences of the United States of America.

Free energies for refolding of the common beta turn into the inverse-common beta turn: simulation of the role of D/L chirality.

A. Tropsha, B. W. Erickson, J. Hermans, 1993, Proceedings of the National Academy of Sciences of the United States of America.

Free Energies for Folding and Refolding of Four Types of .beta. Turns: Simulation of the Role of D/L Chirality

A. Tropsha, B. W. Erickson, Yibin Yan, 1995 .

Reproducibility, sharing and progress in nanomaterial databases.

A. Tropsha, A. Hickey, K. Mills, 2017, Nature nanotechnology.

A. Tropsha, V. Setola, B. Roth, 2012 .

Structure‐based function inference using protein family‐specific fingerprints

J. Snoeyink, Jun Huan, Wei Wang, 2006, Protein science : a publication of the Protein Society.

Identifying disease-related expressions in reviews using conditional random fields

Elena Tutubalina, Z. Sh. Miftahutdinov, Alexander E. Tropsha, 2017 .