Alexander Sedykh

发表

Hybrid in silico models for drug‐induced liver injury using chemical descriptors and in vitro cell‐imaging information

Xiangwei Zhu, Alexander Sedykh, Shu-Shen Liu, 2014, Journal of applied toxicology : JAT.

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

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

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.

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.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

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

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.

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.

Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

Marlene T. Kim, Suman K. Chakravarti, Roustem D. Saiakhov, 2013, Pharmaceutical Research.

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

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

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

Marlene T. Kim, Hao Zhu, Alexander Sedykh, 2015, Pharmaceutical Research.

Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data

Ruili Huang, Menghang Xia, Marlene T. Kim, 2015, Environmental health perspectives.

Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Hao Zhu, Alexander Sedykh, Bing Yan, 2020, Ecotoxicology and environmental safety.

Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

S. Auerbach, Ruili Huang, Alexander Sedykh, 2017, PloS one.

Correction: Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

S. Auerbach, Ruili Huang, Alexander Sedykh, 2017, PloS one.

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

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

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay.

K. Cross, Alexander Sedykh, N. Kruhlak, 2020, Regulatory toxicology and pharmacology : RTP.

Antitumor agents. 280. Multidrug resistance-selective desmosdumotin B analogues.

Alexander Sedykh, Hao Zhu, K. Lee, 2010, Journal of medicinal chemistry.

Using Tox21 High-Throughput Screening Assays for the Evaluation of Botanical and Dietary Supplements

Nisha S. Sipes, S. Auerbach, Ruili Huang, 2018, Applied in vitro toxicology.

Using Tox21 High-Throughput Screening Assays for the Evaluation of Botanical and Dietary Supplements

S. Auerbach, Ruili Huang, Alexander Sedykh, 2019, Applied in vitro toxicology.

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.

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..

Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays.

Ruili Huang, Raymond R Tice, Menghang Xia, 2019, Chemical research in toxicology.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2015, Pharmaceutical Research.

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.

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.

Alarms about structural alerts.

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

An Integrated Chemical Environment to Support 21st-Century Toxicology

David Allen, Shannon M. Bell, Andy Shapiro, 2017, Environmental health perspectives.

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.

BMDExpress 2: enhanced transcriptomic dose-response analysis workflow

Russell S. Thomas, Byron Kuo, Carole L. Yauk, 2018, Bioinform..

Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling.

Hao Zhu, Wenyi Wang, Marlene T Kim, 2016, Methods in molecular biology.

Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2014, Pharmaceutical Research.

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.

Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants

Hao Zhu, Jun Zhang, Marlene T. Kim, 2014, Chemical research in toxicology.

Predicting chemical ocular toxicity using a combinatorial QSAR approach.

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2012, Chemical research in toxicology.

Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

Alexander Sedykh, Hao Zhu, B. Yan, 2020, Nature Communications.

In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches.

Hao Zhu, Xiaoli Zhao, Bing Yan, 2019, Nanoscale.

Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.

Daniel P Russo, Daniel P. Russo, Hainan Sun, 2017, ACS nano.

A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays

Ruili Huang, Raymond R Tice, Menghang Xia, 2015, Journal of biomolecular screening.

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.

Quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Alexander Sedykh, A. Sagingalieva, Karan Pinto, 2023, ArXiv.

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation

Brian E. Howard, Brian E Howard, Arpit Tandon, 2020, Environment international.

Quantum machine learning for image classification

Alexander Sedykh, A. Sagingalieva, A. Melnikov, 2023, ArXiv.

Universal nanohydrophobicity predictions using virtual nanoparticle library

Hao Zhu, Wenyi Wang, Xiaoli Zhao, 2019, Journal of Cheminformatics.

Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions.

S. Auerbach, Ruchir R. Shah, Alexander Sedykh, 2020, Chemical research in toxicology.

Harnessing In Silico, In Vitro, and In Vivo Data to Understand the Toxicity Landscape of Polycyclic Aromatic Compounds (PACs).

S. Auerbach, Alexander Sedykh, J. Hsieh, 2020, Chemical research in toxicology.

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers

Hao Zhu, Qian Shi, Liying Zhang, 2014, Journal of Computer-Aided Molecular Design.

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

Inta Liepina, Alexander Golbraikh, Alexander Tropsha, 2014 .

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

Hao Zhu, Wenyi Wang, Linlin Zhao, 2017, ACS omega.

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..

In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches.

Alexander Sedykh, B. Yan, Hao Zhu, 2019, Nanoscale.

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Alexander Sedykh, Karan Pinto, M. Pflitsch, 2023, Machine Learning: Science and Technology.

Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

Wenyi Wang, Hao Zhu, Bing Yan, 2020, Nature Communications.

Toxicokinetic Triage for Environmental Chemicals.

Robert G. Pearce, Cory L. Strope, R. Judson, 2015, Toxicological sciences : an official journal of the Society of Toxicology.