Per Aspera Ad Astra : Application of Simplex Qsar Approach in Antiviral Research

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Investigation of anticancer activity of macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure

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2D QSAR Consensus Prediction for High-Throughput Virtual Screening. An Application to COX-2 Inhibition Modeling and Screening of the NCI Database

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Quantitative Structure−Activity Relationship Studies of [(Biphenyloxy)propyl]isoxazole Derivatives. Inhibitors of Human Rhinovirus 2 Replication

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The effect of nitroaromatics’ composition on their toxicity in vivo: Novel, efficient non-additive 1D QSAR analysis

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Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

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QSAR Analysis of Poliovirus Inhibition By Dual Combinations of Antivirals

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QSAR Analysis of Anti-Coxsackievirus B3 Nancy Activity of 2-Amino-3-Nitropyrazole[1,5-α]Pyrimidines by Means of Simplex Approach

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Hit QSAR Analysis of Anti-Coxsackievirus B3 Activity of [(Biphenyloxy)Propyl]Isoxazole Derivatives

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Molecular Design of Active Antiherpetic Compounds Using Hierarchic QSAR Technology

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QSAR Analysis of Influence of Artifical Ribonucleases Structure on their Anti-influenza Activity

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Artificial ribonucleases: Quantitative analysis of the structure-activity relationship and a new insight into the strategy of design of highly efficient RNase mimetics

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QSAR Analysis of Cytotoxicity in HeLa Cells

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2D QSAR Consensus Prediction for High‐Throughput Virtual Screening. An Application to COX‐2 Inhibition Modeling and Screening of the NCI Database.

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QSAR Studies On [(Biphenyloxy)propyl]isoxazole Derivatives With Anti-rhinovirus 2 Activity

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Antiviral Activity of Tetrahydro-2(1H)-Pyrimidinones and Related Compounds: Classification SAR Study

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Computer-aided Drug Design of Novel Anti-coxsackievirus B3 Nancy Agents by Means of Hit QSAR

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

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QSAR without borders

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works

Chalcone Derivatives: Promising Starting Points for Drug Design

Gomes, M. D. N., Muratov, E. N., Pereira, M., Peixoto, J. D. C., Rosseto, L. P., Cravo, P., Andrade, C. H., & Neves, B. J. (2017) · Molecules · doi:10.3390/molecules22081210 · Cited by 432 · see citing works

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

Isayev, O., Fourches, D., Muratov, E. N., Oses, C., Rasch, K., Tropsha, A., & Curtarolo, S. (2015) · Chemistry of Materials · doi:10.1021/cm503507h · Cited by 300 · see citing works

Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future

Polishchuk, P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00274 · Cited by 236 · see citing works

Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., Sedykh, A., Muratov, E. N., Кузьмин, В. Е., Fourches, D., et al. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200148a · Cited by 224 · see citing works

Alarms about structural alerts

Alves, V. M., Muratov, E. N., Capuzzi, S. J., Politi, R., Low, Y., Braga, R. C., Zakharov, A. V., Sedykh, A., Mokshyna, E., Farag, S., et al. (2016) · Green Chemistry · doi:10.1039/c6gc01492e · Cited by 133 · see citing works

Existing and Developing Approaches for QSAR Analysis of Mixtures

Muratov, E. N., Varlamova, E. V., Artemenko, A. G., Полищук, П. Г., & Kuz’min, V. Е. (2012) · Molecular Informatics · doi:10.1002/minf.201100129 · Cited by 131 · see citing works

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

Alves, V. M., Muratov, E. N., Fourches, D., Strickland, J. A., Kleinstreuer, N., Andrade, C. H., & Tropsha, A. (2015) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2014.12.014 · Cited by 99 · see citing works

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

Alves, V. M., Bobrowski, T., Melo-Filho, C. C., Korn, D., Auerbach, S. S., Schmitt, C., Muratov, E. N., & Tropsha, A. (2020) · Molecular Informatics · doi:10.1002/minf.202000113 · Cited by 95 · see citing works

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Chen, C., Tanaka, K., Kotera, M., & Funatsu, K. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0417-9 · Cited by 93 · see citing works

Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers

Borba, J. V. V. B., Braga, R. C., Alves, V. M., Muratov, E. N., Kleinstreuer, N., Tropsha, A., & Andrade, C. H. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00186 · Cited by 87 · see citing works

Interpretation of QSAR Models Based on Random Forest Methods

Kuz’min, V. Е., Полищук, П. Г., Artemenko, A. G., & Andronati, S. A. (2011) · Molecular Informatics · doi:10.1002/minf.201000173 · Cited by 84 · see citing works

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

Alves, V. M., Muratov, E. N., Fourches, D., Strickland, J. A., Kleinstreuer, N., Andrade, C. H., & Tropsha, A. (2015) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2014.12.013 · Cited by 83 · see citing works

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

Capuzzi, S. J., Politi, R., Isayev, O., Farag, S., & Tropsha, A. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2016.00003 · Cited by 79 · see citing works

QSAR Modeling and Prediction of Drug–Drug Interactions

Zakharov, A. V., Varlamova, E. V., Lagunin, A. A., Dmitriev, A. V., Muratov, E. N., Fourches, D., Kuz’min, V. Е., Poroikov, V., Tropsha, A., & Nicklaus, M. C. (2015) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00762 · Cited by 77 · see citing works

Universal Approach for Structural Interpretation of QSAR/QSPR Models

Polishchuk, P. G., Kuz'min, V. E., Artemenko, A. G., & Muratov, E. N. (2013) · Molecular Informatics · doi:10.1002/minf.201300029 · Cited by 76 · see citing works

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

Alves, V. M., Muratov, E. N., Zakharov, A. V., MURATOV, N. N., Andrade, C. H., & Tropsha, A. (2017) · Food and Chemical Toxicology · doi:10.1016/j.fct.2017.04.008 · Cited by 75 · see citing works

QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids

Oprisiu, I., Varlamova, E. V., Muratov, E. N., Artemenko, A. G., Marcou, G., Полищук, П. Г., Kuz’min, V. Е., & Varnek, A. A. (2012) · Molecular Informatics · doi:10.1002/minf.201200006 · Cited by 69 · see citing works

Computational approaches for skin sensitization prediction

Wilm, A., Kühnl, J., & Kirchmair, J. (2018) · Critical Reviews in Toxicology · doi:10.1080/10408444.2018.1528207 · Cited by 64 · see citing works

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

Alves, V. M., Hwang, D., Muratov, E., Sokolsky-Papkov, M., Varlamova, E., Vinod, N., Lim, C., Andrade, C. H., Tropsha, A., & Kabanov, A. (2019) · Science Advances · doi:10.1126/sciadv.aav9784 · Cited by 56 · see citing works

Consensus of classification trees for skin sensitisation hazard prediction

Asturiol, D., Casati, S., & Worth, A. P. (2016) · Toxicology in Vitro · doi:10.1016/j.tiv.2016.07.014 · Cited by 56 · see citing works

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

Artemenko, A. G., Muratov, E. N., Kuz’min, V. Е., MURATOV, N. N., Varlamova, E. V., Kuz’mina, A. V., Gorb, L., Golius, A., Hill, F. C., Leszczyński, J., et al. (2011) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2011.569950 · Cited by 55 · see citing works

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

Alves, V. M., Golbraikh, A., Capuzzi, S. J., Liu, K., Lam, W. I., Korn, D., Pozefsky, D., Andrade, C. H., Muratov, E. N., & Tropsha, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00124 · Cited by 53 · see citing works

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

Alves, V. M., Capuzzi, S. J., Braga, R. C., Borba, J. V. V. B., Silva, A. C., Luechtefeld, T., Härtung, T., Andrade, C. H., Muratov, E. N., & Tropsha, A. (2018) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.7b04220 · Cited by 51 · see citing works

Simplex representation of molecular structure as universal QSAR/QSPR tool

Kuz’min, V. Е., Artemenko, A. G., Ognichenko, L. N., Hromov, A. I., Kosinskaya, A. P., Stelmakh, S. I., Sessions, Z. L., & Muratov, E. N. (2021) · Structural Chemistry · doi:10.1007/s11224-021-01793-z · Cited by 46 · see citing works

Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway

Tan, H., Wu, J., Zhang, R., Zhang, C., Li, W., Chen, Q., Zhang, X., Yu, H., & Shi, W. F. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c02242 · Cited by 39 · see citing works

Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors

Capuzzi, S. J., Sun, W., Muratov, E. N., Martínez-Romero, C., He, S., Zhu, W., Li, H., Tawa, G., Fisher, E. G., Xu, M., et al. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00035 · Cited by 35 · see citing works

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

Sedykh, A., Shah, R., Kleinstreuer, N., Auerbach, S. S., & Gombar, V. K. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00464 · Cited by 33 · see citing works

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases

Andrade, C. H., Neves, B. J., Melo-Filho, C. C., Rodrigues, J., Silva, D. C., Braga, R. C., & Cravo, P. (2018) · Current Medicinal Chemistry · doi:10.2174/0929867325666180309114824 · Cited by 29 · see citing works

Development of quantitative structure-property relationship (QSPR) models for predicting the thermal hazard of ionic liquids: A review of methods and models

Jiang, J., Duan, W., Wei, Q., Zhao, X., Ni, L., Pan, Y. L., & Shu, C. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.112471 · Cited by 25 · see citing works

Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents

Полищук, П. Г., Samoylenko, G. V., Khristova, T. M., Krysko, O. L., Кабанова, Т. А., Kabanov, V. M., Kornylov, A. Y., Klimchuk, O., Langer, T., Andronati, S. A., et al. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00865 · Cited by 23 · see citing works

Modern approaches to accelerate discovery of new antischistosomal drugs

Neves, B. J., Muratov, E. N., Machado, R. B., Andrade, C. H., & Cravo, P. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1178230 · Cited by 23 · see citing works

QSAR analysis of poliovirus inhibition by dual combinations of antivirals

Muratov, E. N., Varlamova, E. V., Artemenko, A. G., Полищук, П. Г., Nikolaeva–Glomb, L., Galabov, A. S., & Kuz’min, V. Е. (2013) · Structural Chemistry · doi:10.1007/s11224-012-0195-8 · Cited by 21 · see citing works

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

Fourches, D., Muratov, E. N., Ding, F., Dokholyan, N. V., & Tropsha, A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400216q · Cited by 21 · see citing works

Prediction of the flash points of binary biodiesel mixtures from molecular structures

Yao, J., Qi, R., Pan, Y., He, H., Fan, Y., Jiang, J., & Jiang, J. (2020) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2020.104137 · Cited by 19 · see citing works

Qsar Analysis of [(Biphenyloxy)Propyl]Isoxazoles: Agents Against Coxsackievirus B3

Muratov, E. N., Varlamova, E. V., Artemenko, A. G., Khristova, T., Kuz’min, V. Е., Makarov, V., Riabova, O., Wutzler, P., & Schmidtke, M. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.278 · Cited by 19 · see citing works

xMaP—An Interpretable Alignment-Free Four-Dimensional Quantitative Structure–Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles

Dreher, J., Scheiber, J., Stiefl, N., & Baumann, K. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00419 · Cited by 18 · see citing works

Dispirotripiperazine-core compounds, their biological activity with a focus on broad antiviral property, and perspectives in drug design (mini-review)

Egorova, A., Bogner, E., Novoselova, E., Zorn, K. M., Ekins, S., & Makarov, V. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.113014 · Cited by 18 · see citing works

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

Thompson, C. G., Sedykh, A., Nicol, M. R., Muratov, E. N., Fourches, D., Tropsha, A., & Kashuba, A. D. M. (2014) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2013.0254 · Cited by 17 · see citing works

A novel method for predicting the flash points of binary mixtures from molecular structures

Cao, W., Pan, Y., Liu, Y., & Jiang, J. (2020) · Safety Science · doi:10.1016/j.ssci.2020.104680 · Cited by 16 · see citing works

Efficient design of peptide-binding polymers using active learning approaches

Rakhimbekova, A., Lopukhov, A. V., Klyachko, N. L., Kabanov, A. V., Madzhidov, T., & Tropsha, A. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.11.023 · Cited by 14 · see citing works

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures

Shen, S., Pan, Y. L., Ji, X., Ni, Y., & Jiang, J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20092084 · Cited by 14 · see citing works

QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure

Ognichenko, L. N., Kuz’min, V. Е., Gorb, L., Hill, F. C., Artemenko, A. G., Polischuk, P. G., & Leszczyński, J. (2012) · Molecular Informatics · doi:10.1002/minf.201100102 · Cited by 12 · see citing works

QSPR modeling of critical properties of organic binary mixtures

Mokshyna, O., Полищук, П. Г., Недоступ, В. И., & Kuz’min, V. Е. (2016) · Russian Journal of Organic Chemistry · doi:10.1134/s1070428016010024 · Cited by 9 · see citing works

Drug design of new 5-HT6R antagonists aided by artificial neural networks

da Silva, A. P., Chiari, L. P. A., Guimarães, A. R., Honorio, K. M., & da Silva, A. B. F. (2021) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2021.107844 · Cited by 8 · see citing works

Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil

Silva, L. D. M. E., Lorenzo, V. P., Lopes, W. S., Scotti, L., & Scotti, M. T. (2019) · Molecular Informatics · doi:10.1002/minf.201800156 · Cited by 7 · see citing works

Using Molecular Fingerprints as Descriptors in Toxicity Prediction: A Survey

Mahmoud, R. S., & Yousef, A. H. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8982990 · Cited by 7 · see citing works

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

Korn, D., Pervitsky, V., Bobrowski, T., Alves, V. M., Schmitt, C., Bizon, C., Baker, N., Chirkova, R. Y., Cherkasov, A. R., Muratov, E. N., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01285 · Cited by 6 · see citing works

QSAR Analysis of Poliovirus Inhibition By Dual Combinations of Antivirals

Muratov, E. N., Varlamova, E. V., Kuz’min, V. Е., Artemenko, A. G., Nikolaeva–Glomb, L., & Galabov, A. S. (2010) · Antiviral Research · doi:10.1016/j.antiviral.2010.02.456 · Cited by 5 · see citing works

New Advances in QSPR/QSAR Analysis of Nitrocompounds: Solubility, Lipophilicity, and Toxicity

Ognichenko, L. N., Kuz’min, V. Е., Gorb, L., Muratov, E. N., Artemenko, A. G., Kovdienko, N. A., Полищук, П. Г., Hill, F. C., & Leszczyński, J. (2012) · Springer eBooks · doi:10.1007/978-94-007-0923-2_8 · Cited by 4 · see citing works

Analysis and Prediction of the Reproductive Toxicity of Organic Compounds of Different Classes using 2D Simplex Representations of Molecular Structure

Tinkov, O. V., Muratov, E. N., Artemenko, A. G., & Кузьмин, В. Е. (2013) · Pharmaceutical Chemistry Journal · doi:10.1007/s11094-013-0974-6 · Cited by 4 · see citing works

Structure-dependent incorporation of terpenes into amphiphilic Poly(2-oxazoline) micelles

Palchak, L., Kozlov, S., Melo-Filho, C. C., Tropsha, A., & Kabanov, A. V. (2026) · Biomedical Microdevices · doi:10.1007/s10544-026-00823-z · Cited by 3 · see citing works

Quantitative Structure-Property Relationship Analysis of Drugs’ Pharmacokinetics Within the Framework of Biopharmaceutics Classification System Using Simplex Representation of Molecular Structure

Golovenko, N. Y., Borysiuk, I., Kulinskiy, М. А., Полищук, П. Г., Muratov, E. N., & Kuz’min, V. Е. (2014) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-94-017-9257-8_14 · Cited by 3 · see citing works

The effect of the structure of derivatives of nitrogen-containing heterocycles on their anti-influenza activity

Гридина, Т. Л., Федчук, А. С., Basok, S. S., Artemenko, A. G., Ognichenko, L. N., Шитикова, Л. И., Lutsyuk, A. F., Грузевский, А. A., & Kuz’min, V. Е. (2019) · Chemistry of Heterocyclic Compounds · doi:10.1007/s10593-019-02479-5 · Cited by 3 · see citing works

The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Zhao, G. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/w4vm-f755 · Cited by 2 · see citing works

Virtual Screening and Assessment of Anticancer Potential of Selenium-Based Compounds Against HL-60 and MCF7 Cells

de Queiroz, E. F. F., Luís, J., Dantas, D. D. S., Arruda, L. C. P., da Silva, E. C. B., Silva, J. M. D. S. E., Souza, H. D. D. S., Barros, R. P. C., Costa, L. A. D. M., Filho, P. F. D. A., et al. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0110 · Cited by 2 · see citing works

Quantitative Structure Activity Relationship Analysis of Antiviral Activity of PF74 Type HIV-1 Capsid Protein Inhibitors by Simplex Representation of Molecular Structure

Louis, B., & Agrawal, V. K. (2022) · Polycyclic aromatic compounds · doi:10.1080/10406638.2022.2038215 · Cited by 1 · see citing works

Desenvolvimento de modelos de QSAR e análise quimioinformática da sensibilização e permeabilidade da pele

Alves, V. D. M. (2014) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 1 · see citing works

PREDICTIVE CHEMINFORMATICS ANALYSIS OF DIVERSE CHEMOGENOMICS DATA SOURCES: APPLICATIONS TO DRUG DISCOVERY, ASSAY INTERFERENCE, AND TEXT MINING

Capuzzi, S. J. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/bs3n-4f22

Additional file 1 of Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Chen, C.-H., Tanaka, K., Kotera, M., & 船津, 公. (2020) · Figshare · doi:10.6084/m9.figshare.12050823

Machine learning-based QSPR modeling for predicting the n-octanol/air partition coefficient of polybrominated diphenyl ethers

Wu, W., Chen, H., Liu, Z., Yan, W., Wu, D., Chen, J., Liu, J., & Xing, Z. (2026) · iScience · doi:10.1016/j.isci.2026.115393

Computer-aided drug design of broad-spectrum antiviral compounds

Klimenko, K. (2017) · doi:10.70675/5f1dee56z0ae7z4702zbd92z03596274e8f9

Predictive models for kinetic and thermodynamic parameters of reactions

Gimadiev, T. R. (2018) · doi:10.70675/ab76b9fcze4cbz45a3z950dz023b6506d0ac

BindingDB Entry 50046668: Design, Virtual Screening, and Synthesis of Antagonists ofaIIbß3 as Antiplatelet Agents.

BindingDB (2016) · UC San Diego · doi:10.7270/q2qn68mb

Hierarchical QSAR technology based on the Simplex representation of molecular structure

Kuz’min, V. Е., Artemenko, A. G., & Muratov, E. N. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9179-6 · Cited by 153 · see citing works

Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

Kuz’min, V. Е., Artemenko, A. G., Muratov, E. N., Polischuk, P. G., Ognichenko, L. N., Liahovsky, A. V., Hromov, A. I., & Varlamova, E. V. (2009) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-1-4020-9783-6_5 · Cited by 31 · see citing works

Hierarchic system of QSAR models (1D–4D) on the base of simplex representation of molecular structure

Kuz’min, V. Е., Artemenko, A. G., Polischuk, P. G., Muratov, E. N., Hromov, A. I., Liahovsky, A. V., Andronati, S. A., & Makan, S. Y. (2005) · Journal of Molecular Modeling · doi:10.1007/s00894-005-0237-x · Cited by 98 · see citing works

Simplex representation of molecular structure as universal QSAR/QSPR tool

Kuz’min, V. Е., Artemenko, A. G., Ognichenko, L. N., Hromov, A. I., Kosinskaya, A. P., Stelmakh, S. I., Sessions, Z. L., & Muratov, E. N. (2021) · Structural Chemistry · doi:10.1007/s11224-021-01793-z · Cited by 46 · see citing works

Structural, Physicochemical and Stereochemical Interpretation of QSAR Models Based on Simplex Representation of Molecular Structure

Полищук, П. Г., Mokshyna, O., Kosinskaya, A. P., Muats, A., Kulinskiy, М. А., Tinkov, O. V., Ognichenko, L. N., Khristova, T., Artemenko, A. G., & Kuz’min, V. Е. (2017) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-319-56850-8_4 · Cited by 5 · see citing works