Alexander Tropsha

发表

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 .

Deep reinforcement learning for de novo drug design

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

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..

Thermomechanical properties in the AFLOW distributed database

Marco Buongiorno Nardelli, Cormac Toher, Corey Oses, 2017 .

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.

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

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..

Rapid protein structure classification using one-dimensional structure profiles on the bioSCAN parallel computer

Iosif I. Vaisman, D. L. Hoffman, Alexander Tropsha, 1995, Comput. Appl. Biosci..

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..

Fishing out the signal in polypharmacological high-throughput screening data using novel navigator cheminformatics software

Alexander Tropsha, Denis Fourches, 2014, Journal of Cheminformatics.

QSAR without borders.

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

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

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

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

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

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

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

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..

Chemistry-wide association studies (CWAS) to determine joint toxicity effects of co-occurring chemical features

Alexander Tropsha, Denis Fourches, Alexander Sedykh, 2014, Journal of Cheminformatics.

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.

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..

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.

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..

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..

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

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

Chemotext: A Publicly Available Web Server for Mining Drug-Target-Disease Relationships in PubMed

Colin P O'Banion, Alexander Tropsha, Diane Pozefsky, 2018, J. Chem. Inf. Model..

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..

Session Introduction: Biogeometry: Applications of Computational Geometry to Molecular Structure

Herbert Edelsbrunner, Alexander Tropsha, 2005, Pacific Symposium on Biocomputing.

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..

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..

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..

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..

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

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

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..

Generalized linear response method: Application to hydration free energy calculations

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

Recent trends in statistical QSAR modeling of environmental chemical toxicity.

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

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

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

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.

Best Practices for QSAR Model Development, Validation, and Exploitation

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

Chembench: A Publicly Accessible, Integrated Cheminformatics Portal

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

Data Set Modelability by QSAR

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

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.

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

Alexander Golbraikh, Alexander Tropsha, Min Shen, 2004, Journal of medicinal chemistry.

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

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

Universal fragment descriptors for predicting properties of inorganic crystals

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

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

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

Materials Informatics

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

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.

Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets

Nikolay V. Dokholyan, Alexander Tropsha, Shubin Liu, 2011, 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.

Data Sharing and the Electronic NanoBook (eNanobook)

Alexander Tropsha, Fred Prior, 2017 .

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..

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.

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..

Computer Aided Drug Design

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

Automated Pharmacophore Identification for Large Chemical Data Sets1

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

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..

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

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

Chapter 10:Integrated Chemo- and Bioinformatics Approaches to Virtual Screening

Alexander Tropsha, 2008 .

Materials Informatics

Alexander Tropsha, Hanoch Senderowitz, A. Tropsha, 2018, 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..

Tuning HERG out: antitarget QSAR models for drug development.

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

Combinatorial QSAR of Ambergris Fragrance Compounds

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

Novel Chirality Descriptors Derived from Molecular Topology

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

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..

Letter from the Editors

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

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

Alexander Tropsha, A. Tropsha, 2006 .

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..

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.

Oy Vey! A Comment on "Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships Outperforming Animal Test Reproducibility".

Ivan Rusyn, Alexander Tropsha, Eugene Muratov, 2019, Toxicological sciences : an official journal of the Society of Toxicology.

Chemometric Analysis of Ligand Receptor Complementarity: Identifying Complementary Ligands Based on Receptor Information (CoLiBRI)

Alexander Tropsha, Curt M. Breneman, Nagamani Sukumar, 2006, J. Chem. Inf. Model..

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.

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..

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.

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..

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

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

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..

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..

ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering

Karamarie Fecho, Alexander Tropsha, Patrick Wang, 2019, Bioinform..

Rational principles of compound selection for combinatorial library design.

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

Mining protein family specific residue packing patterns from protein structure graphs

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

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..

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..

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.

Chemistry, information and Frank: a tribute to Frank Brown

Chris L. Waller, Dimitris K. Agrafiotis, Terry R. Stouch, 2018, Journal of Computer-Aided Molecular Design.

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..

Curation of chemogenomics data.

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

Comparison of applicability domains of QSAR models: application to the modelling of the environmental toxicity against Tetrahymena pyriformis

Igor V Tetko, P Gramatica, Alexander Tropsha, 2008, Chemistry Central Journal.

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

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

Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches

Feng Ding, Nikolay V. Dokholyan, Alexander Tropsha, 2013, 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.

k Nearest Neighbors QSAR Modeling as a Variational Problem: Theory and Applications

Alexander Tropsha, Peter Itskowitz, 2005, J. Chem. Inf. Model..

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

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

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

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

QSAR Modeling Using Chirality Descriptors Derived from Molecular Topology

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

Potential of short-term biological assays to quantitatively predict chronic toxicity

Alexander Tropsha, 2013 .

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..

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..

Uncommon Data Sources for QSAR Modeling

Alexander Tropsha, 2018 .

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..

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.

Functional neighbors: inferring relationships between nonhomologous protein families using family-specific packing motifs

Jack Snoeyink, Wei Wang, Jinze Liu, 2010, IEEE Trans. Inf. Technol. Biomed..

Recent Trends in Quantitative Structure‐Activity Relationships

Alexander Tropsha, A. Tropsha, 2003 .

QSAR/QSPR Revisited

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

Development and application of novel computational approaches for computer-assisted drug design (cadd) and protein modeling

Alexander Tropsha, Shuxing Zhang, 2005 .

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.

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

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

Joint Virtual Special Issue on Computational Toxicology

Igor V. Tetko, Alexander Tropsha, Igor V Tetko, 2020, J. Chem. Inf. Model..

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..

Combinatorial QSAR Modeling of P-Glycoprotein Substrates

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

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..

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

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

Chembench: a cheminformatics workbench

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

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.

Accurate Classification of Protein Structural Families Using Coherent Subgraph Analysis

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

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

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

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..

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..

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..

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.

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

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

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

Olexandr Isayev, Boris Ginsburg, Alexander Tropsha, 2021, 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..

A topological characterization of protein structure

Alexander Tropsha, Bala Krishnamoorthy, Scott Provan, 2007 .

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.

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.

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.

ehp CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

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

Why Academic Drug Discovery Makes Sense

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

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

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

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.

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

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

Chemical basis of interactions between engineered nanoparticles and biological systems.

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

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

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

Using Graph Indices for the Analysis and Comparison of Chemical Datasets

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

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.

QSAR Modeling and Prediction of Drug-Drug Interactions.

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

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.

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.

Distance Dependence of Water Structure around Model Solutes

Iosif I. Vaisman, Alexander Tropsha, Frank K. Brown, 1994 .

Structure‐based function inference using protein family‐specific fingerprints

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

Rational design of a targeted combinatorial chemical library with opiatelike activity

Sung Jin Cho, Alexander Tropsha, Weifan Zheng, 1998 .

Combinatorial QSAR Modeling of P-Glycoprotein Substrates.

Alexander Golbraikh, Alexander Tropsha, Scott Oloff, 2006 .

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

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

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

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

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

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

Cross-Validated R2 Guided Region Selection for CoMFA Studies

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

The effect of region size on CoMFA analyses

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

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

Inta Liepina, Alexander Golbraikh, Alexander Tropsha, 2014 .

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 .

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

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

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.

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

Alexander Golbraikh, Alexander Tropsha, Xiang Simon Wang, 2008 .

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

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

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 .

The AFLOW Fleet for Materials Discovery

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

Generalized linear response method: Application to hydration free energy calculations

Alexander Tropsha, Xin Chen, 1999 .

A bibliometric review of drug repurposing.

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

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

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

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.

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.

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

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

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.

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

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

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.

Pseudotorsional OCCO backbone angle as a single descriptor of protein secondary structure

Alexander Tropsha, A. Tropsha, I. Vaisman, 1995, Protein science : a publication of the Protein Society.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

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

Universal Fragment Descriptors for Predicting Electronic and Mechanical Properties of Inorganic Crystals

Cormac Toher, Corey Oses, Stefano Curtarolo, 2017 .

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.

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.

Quantitative Nanostructure–Activity Relationships: From Unstructured Data to Predictive Models for Designing Nanomaterials with Controlled Properties

Alexander Tropsha, Denis Fourches, 2014 .

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Igor V. Tetko, Artem Cherkasov, Paola Gramatica, 2008, Journal of Chemical Information and Modeling.

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

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

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.

A Turning Point For Blood–Brain Barrier Modeling

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

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.

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.

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

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

Development of kNN QSAR Models for 3-Arylisoquinoline Antitumor Agents

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

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

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

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.

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

Alexander Tropsha, Lu Wang, Jan Hermans, 1998 .

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.

Systems chemical biology.

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

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).

Novel Chirality Descriptors Derived from Molecular Topology.

Alexander Golbraikh, Alexander Tropsha, Danail Bonchev, 2001 .

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

Olexandr Isayev, Alexander Tropsha, Rene Lopez, 2016 .

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.

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.