Denis Fourches

Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., … Tropsha, A. (2021, July 2). A critical overview of computational approaches employed for COVID-19 drug discovery. CHEMICAL SOCIETY REVIEWS. https://doi.org/10.1039/d0cs01065k

Mansouri, K., Karmaus, A. L., Fitzpatrick, J., Patlewicz, G., Pradeep, P., Alberga, D., … Kleinstreuer, N. C. (2021). CATMoS: Collaborative Acute Toxicity Modeling Suite. ENVIRONMENTAL HEALTH PERSPECTIVES, 129(4). https://doi.org/10.1289/EHP8495

Borrel, A., Melander, C., & Fourches, D. (2021). Cheminformatics Analysis of Fluoroquinolones and Their Inhibition Potency Against Four Pathogens. MOLECULAR INFORMATICS, 40(5). https://doi.org/10.1002/minf.202000215

Li, X., & Fourches, D. (2021). SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 61(4), 1560–1569. https://doi.org/10.1021/acs.jcim.0c01127

Takeda, K., Ikenaka, Y., Fourches, D., Tanaka, K. D., Nakayama, S. M. M., Triki, D., … Ishizuka, M. (2021). The VKORC1 ER-luminal loop mutation (Leu76Pro) leads to a significant resistance to warfarin in black rats (Rattus rattus). PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY, 173. https://doi.org/10.1016/j.pestbp.2021.104774

Zin, P. P. K., Borrel, A., & Fourches, D. (2020). Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict? JOURNAL OF CHEMICAL INFORMATION AND MODELING, 60(7), 3342–3360. https://doi.org/10.1021/acs.jcim.0c00200

Mansouri, K., Kleinstreuer, N., Abdelaziz, A. M., Alberga, D., Alves, V. M., Andersson, P. L., … Judson, R. S. (2020). CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES, 128(2). https://doi.org/10.1289/EHP5580

Li, X., Kleinstreuer, N. C., & Fourches, D. (2020). Hierarchical Quantitative Structure-Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity. CHEMICAL RESEARCH IN TOXICOLOGY, 33(2), 353–366. https://doi.org/10.1021/acs.chemrestox.9b00259

Reich, B. J., Guan, Y., Fourches, D., Warren, J. L., Sarnat, S. E., & Chang, H. H. (2020). INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH. ANNALS OF APPLIED STATISTICS, 14(4), 1945–1963. https://doi.org/10.1214/20-AOAS1364

Cools, F., Triki, D., Geerts, N., Delputte, P., Fourches, D., & Cos, P. (2020). In vitroandin vivoEvaluation ofin silicoPredicted Pneumococcal UDPG:PP Inhibitors. FRONTIERS IN MICROBIOLOGY, 11. https://doi.org/10.3389/fmicb.2020.01596

Li, X., & Fourches, D. (2020). Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT. JOURNAL OF CHEMINFORMATICS, 12(1). https://doi.org/10.1186/s13321-020-00430-x

Reese, H. R., Xiao, X., Shanahan, C. C., Driessche, G. A., Fourches, D., Carbonell, R. G., … Menegatti, S. (2020). Novel peptide ligands for antibody purification provide superior clearance of host cell protein impurities. JOURNAL OF CHROMATOGRAPHY A, 1625. https://doi.org/10.1016/j.chroma.2020.461237

Day, K., Schneible, J. D., Young, A. T., Pozdin, V. A., Driessche, G., Gaffney, L. A., … Menegatti, S. (2020). Photoinduced reconfiguration to control the protein-binding affinity of azobenzene-cyclized peptides. JOURNAL OF MATERIALS CHEMISTRY B, 8(33), 7413–7427. https://doi.org/10.1039/d0tb01189d

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., … Tropsha, A. (2020). [Review of QSAR without borders]. CHEMICAL SOCIETY REVIEWS, 49(11), 3525–3564. https://doi.org/10.1039/d0cs00098a

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., … Tropsha, A. (2020, June 7). QSAR without borders (vol 10, pg 531, 2020). CHEMICAL SOCIETY REVIEWS, Vol. 49, pp. 3716–3716. https://doi.org/10.1039/d0cs90041a

Zin, P. P. K., Williams, G., & Fourches, D. (2020). SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides. Journal of Cheminformatics, 12(1). https://doi.org/10.1186/s13321-020-00427-6

Odenkirk, M. T., Zin, P. P. K., Ash, J. R., Reif, D. M., Fourches, D., & Baker, E. S. (2020). Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems. ANALYST, 145(22), 7197–7209. https://doi.org/10.1039/d0an01638a

Singam, E. R. A., Tachachartvanich, P., Fourches, D., Soshilov, A., Hsieh, J. C. Y., La Merrill, M. A., … Durkin, K. A. (2020). Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning. ENVIRONMENTAL RESEARCH, 190. https://doi.org/10.1016/j.envres.2020.109920

Odenkirk, M. T., Stratton, K. G., Gritsenko, M. A., Bramer, L. M., Webb-Robertson, B.-J. M., Bloodsworth, K. J., … Baker, E. S. (2020). Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools. MOLECULAR OMICS, 16(6). https://doi.org/10.1039/d0mo00074d

Fourches, D., & Ash, J. (2019). [Review of 4D-quantitative structure-activity relationship modeling: making a comeback]. EXPERT OPINION ON DRUG DISCOVERY, 14(12), 1227–1235. https://doi.org/10.1080/17460441.2019.1664467

Wen, D., Wang, J., Van Den Driessche, G., Chen, Q., Zhang, Y., Chen, G., … Gu, Z. (2019). Adipocytes as Anticancer Drug Delivery Depot. MATTER, 1(5), 1203–1214. https://doi.org/10.1016/j.matt.2019.08.007

Plundrich, N. J., Cook, B. T., Maleki, S. J., Fourches, D., & Lila, M. A. (2019). Binding of peanut allergen Ara h 2 with Vaccinium fruit polyphenols. FOOD CHEMISTRY, 284, 287–295. https://doi.org/10.1016/j.foodchem.2019.01.081

Ash, J. R., Kuenemann, M. A., Rotroff, D., Motsinger-Reif, A., & Fourches, D. (2019). Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. JOURNAL OF CHEMINFORMATICS, 11. https://doi.org/10.1186/s13321-019-0366-3

Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., … Zucknick, M. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. NATURE COMMUNICATIONS, 10. https://doi.org/10.1038/s41467-019-09799-2

Williams, T. N., Van Den Driessche, G. A., Valery, A. R. B., Fourches, D., & Freeman, H. S. (2019). Corrections to “Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 2. Identification of Hair Dye Substance Database Analogs in the Max Weaver Dye Library.” ACS Sustainable Chemistry & Engineering, 7(1), 1806–1806. https://doi.org/10.1021/ACSSUSCHEMENG.8B05545

West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M. A., Chang, S.-M., Wu, M. C., … Tzeng, J.-Y. (2019). Identifying individual risk rare variants using protein structure guided local tests (POINT). PLOS COMPUTATIONAL BIOLOGY, 15(2). https://doi.org/10.1371/journal.pcbi.1006722

Burnum-Johnson, K. E., Zheng, X., Dodds, J. N., Ash, J., Fourches, D., Nicora, C. D., … Baker, E. S. (2019). [Review of Ion mobility spectrometry and the omics: Distinguishing isomers, molecular classes and contaminant ions in complex samples]. TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 116, 292–299. https://doi.org/10.1016/j.trac.2019.04.022

Fourches, D., & Feducia, J. (2019). Student-Guided Three-Dimensional Printing Activity in Large Lecture Courses: A Practical Guideline. JOURNAL OF CHEMICAL EDUCATION, 96(2), 291–295. https://doi.org/10.1021/acs.jchemed.8b00346

Van Den Driessche, G., & Fourches, D. (2018). Adverse drug reactions triggered by the common HLA-B*57:01 variant: Virtual screening of DrugBank using 3D molecular docking. Journal of Cheminformatics, 10.

Kuenemann, M. A., & Fourches, D. (2018). Cheminformatics Analysis of Dynamic WNK-Inhibitor Interactions. Molecular Informatics, 37(6-7), 1700138. https://doi.org/10.1002/MINF.201700138

Zin, P. P. K., Williams, G., & Fourches, D. (2018). Cheminformatics-based enumeration and analysis of large libraries of macrolide scaffolds. Journal of Cheminformatics, 10(1). https://doi.org/10.1186/s13321-018-0307-6

Low, Y. S., Alves, V. M., Fourches, D., Sedykh, A., Andrade, C. H., Muratov, E. N., … Tropsha, A. (2018). Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 58(11), 2203–2213. https://doi.org/10.1021/acs.jcim.8b00450

Mahapatra, D., Franzosa, J. A., Roell, K., Kuenemann, M. A., Houck, K. A., Reif, D. M., … Kullman, S. W. (2018). Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays. Scientific Reports, 8(1). https://doi.org/10.1038/S41598-018-27055-3

Borrel, A., Kleinstreuer, N. C., & Fourches, D. (2018). Exploring drug space with ChemMaps.com. BIOINFORMATICS, 34(21), 3773–3775. https://doi.org/10.1093/bioinformatics/bty412

Kuenemann, M. A., Spears, P. A., Orndorff, P. E., & Fourches, D. (2018). In silico
 Predicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required for Listeria
 Virulence. Molecular Informatics, 37(6-7), 1800004. https://doi.org/10.1002/MINF.201800004

Sanabria-Ojeda, L., Fukuyama, T., Fourches, D., & Baumer, W. (2018). Janus kinase inhibitors differ in their affinity to the TRPV1 receptor - implications for their use in itch and pain. Journal of Veterinary Pharmacology and Therapeutics, 41, 160–160.

La, M. K., Sedykh, A., Fourches, D., Muratov, E., & Tropsha, A. (2018). Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions. DRUG SAFETY, 41(11), 1059–1072. https://doi.org/10.1007/s40264-018-0688-5

Williams, T. N., Kuenemann, M. A., Driessche, G. A., Williams, A. J., Fourches, D., & Freeman, H. S. (2018). Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 1. Database Development and Analysis. ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 6(2), 2344–2352. https://doi.org/10.1021/acssuschemeng.7b03795

Williams, T. N., Driessche, G. A., Valery, A. R. B., Fourches, D., & Freeman, H. S. (2018). Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 2. Identification of Hair Dye Substance Database Analogs in the Max Weaver Dye Library. ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 6(11), 14248–14256. https://doi.org/10.1021/acssuschemeng.8b02882

Driessche, G., & Fourches, D. (2017). Adverse drug reactions triggered by the common HLA-B*57:01 variant: A molecular docking study. Journal of Cheminformatics, 9.

Ash, J., & Fourches, D. (2017). Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 57(6), 1286–1299. https://doi.org/10.1021/acs.jcim.7b00048

Kuenemann, M. A., & Fourches, D. (2017). Cheminformatics Modeling of Amine Solutions for Assessing their CO2Absorption Properties. Molecular Informatics, 36(7), 1600143. https://doi.org/10.1002/MINF.201600143

Kuenemann, M. A., & Fourches, D. (2017). Cheminformatics modeling of amine solutions for assessing their CO2 absorption properties. Molecular Informatics, 36(7).

Muratov, E., Lewis, M., Fourches, D., Tropsha, A., & Cox, W. C. (2017). Computer-assisted decision support for student admissions based on their predicted academic performance. American Journal of Pharmaceutical Education, 81(3).

Legge, S. E., Hamshere, M. L., Ripke, S., Pardinas, A. F., Goldstein, J. I., Rees, E., … Walters, J. T. R. (2017). Genome-wide common and rare variant analysis provides novel insights into clozapine-associated neutropenia. Molecular Psychiatry, 22(10), 1502–1508. https://doi.org/10.1038/MP.2016.97

Fourches, D. (2017). Reaction: Molecular Modeling for Novel Antibacterials. Chem, 3(1), 13–14. https://doi.org/10.1016/J.CHEMPR.2017.06.016

Borrel, A., & Fourches, D. (2017). RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality. BIOINFORMATICS, 33(23), 3816–3818. https://doi.org/10.1093/bioinformatics/btx485

Kuenemann, M. A., Szymczyk, M., Chen, Y., Sultana, N., Hinks, D., Freeman, H. S., … Vinueza, N. R. (2017). Weaver's historic accessible collection of synthetic dyes: a cheminformatics analysis. CHEMICAL SCIENCE, 8(6), 4334–4339. https://doi.org/10.1039/c7sc00567a

Borysov, P., Hannig, J., Marron, J. S., Muratov, E., Fourches, D., & Tropsha, A. (2016). Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors. Journal of Chemometrics, 30(3), 99–108. https://doi.org/10.1002/CEM.2776

Alves, V. M., Muratov, E. N., Capuzzi, S. J., Politi, R., Low, Y., Braga, R. C., … Tropsha, A. (2016). Alarms about structural alerts. GREEN CHEMISTRY, 18(16), 4348–4360. https://doi.org/10.1039/c6gc01492e

Elkins, J. M., Fedele, V., Szklarz, M., Azeez, K. R. A., Salah, E., Mikolajczyk, J., … Polley, E. (2016). Comprehensive characterization of the Published Kinase Inhibitor Set. Nature Biotechnology, 34(1), 95–103.

Fourches, D., Pu, D., Li, L., Zhou, H., Mu, Q., Su, G., … Tropsha, A. (2016). Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. NANOTOXICOLOGY, 10(3), 374–383. https://doi.org/10.3109/17435390.2015.1073397

Zakharov, A. V., Varlamova, E. V., Lagunin, A. A., Dmitriev, A. V., Muratov, E. N., Fourches, D., … Nicklaus, M. C. (2016). QSAR Modeling and Prediction of Drug-Drug Interactions. MOLECULAR PHARMACEUTICS, 13(2), 545–556. https://doi.org/10.1021/acs.molpharmaceut.5b00762

Alves, V. M., Capuzzi, S. J., Muratov, E. N., Braga, R. C., Thornton, T. E., Fourches, D., … Tropsha, A. (2016). QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. GREEN CHEMISTRY, 18(24), 6501–6515. https://doi.org/10.1039/c6gc01836j

Fourches, D., Muratov, E., & Tropsha, A. (2016). Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 56(7), 1243–1252. https://doi.org/10.1021/acs.jcim.6b00129

Elkins, J. M., Fedele, V., Szklarz, M., Abdul Azeez, K. R., Salah, E., Mikolajczyk, J., … Zuercher, W. J. (2015). Comprehensive characterization of the Published Kinase Inhibitor Set. Nature Biotechnology, 34(1), 95–103. https://doi.org/10.1038/NBT.3374

Fourches, D., Muratov, E., & Tropsha, A. (2015, August). Curation of chemogenomics data. https://doi.org/10.1038/nchembio.1881

Baker, N. C., Fourches, D., & Tropsha, A. (2015). Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity. Molecular Informatics, 34(2-3), 160–170. https://doi.org/10.1002/MINF.201400134

Isayev, O., Fourches, D., Muratov, E. N., Oses, C., Rasch, K., Tropsha, A., & Curtarolo, S. (2015). Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints. Chemistry of Materials, 27(3), 735–743. https://doi.org/10.1021/CM503507H

Braga, R. C., Alves, V. M., Silva, M. F. B., Muratov, E., Fourches, D., Liao, L. M., … Andrade, C. H. (2015). Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity. MOLECULAR INFORMATICS, 34(10), 698–701. https://doi.org/10.1002/minf.201500040

Alves, V. M., Muratov, E., Fourches, D., Strickland, J., Kleinstreuer, N., Andrade, C. H., & Tropsha, A. (2015). Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology, 284(2), 262–272. https://doi.org/10.1016/J.TAAP.2014.12.014

Alves, V. M., Muratov, E., Fourches, D., Strickland, J., Kleinstreuer, N., Andrade, C. H., & Tropsha, A. (2015). Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology, 284(2), 273–280. https://doi.org/10.1016/J.TAAP.2014.12.013

Mu, Q., Jiang, G., Chen, L., Zhou, H., Fourches, D., Tropsha, A., & Yan, B. (2014). Chemical Basis of Interactions Between Engineered Nanoparticles and Biological Systems. Chemical Reviews, 114(15), 7740–7781. https://doi.org/10.1021/CR400295A

Goldstein, J. I., Fredrik Jarskog, L., Hilliard, C., Alfirevic, A., Duncan, L., Fourches, D., … Sullivan, P. F. (2014). Clozapine-induced agranulocytosis is associated with rare HLA-DQB1 and HLA-B alleles. Nature Communications, 5(1). https://doi.org/10.1038/NCOMMS5757

Golbraikh, A., Muratov, E., Fourches, D., & Tropsha, A. (2014). Data Set Modelability by QSAR. Journal of Chemical Information and Modeling, 54(1), 1–4. https://doi.org/10.1021/CI400572X

Blatt, J., Farag, S., Corey, S. J., Sarrimanolis, Z., Muratov, E., Fourches, D., … Janzen, W. P. (2014). Expanding the scope of drug repurposing in pediatrics: The Children's Pharmacy Collaborative™. Drug Discovery Today, 19(11), 1696–1698. https://doi.org/10.1016/J.DRUDIS.2014.08.003

Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., … Tropsha, A. (2014). QSAR Modeling: Where Have You Been? Where Are You Going To? Journal of Medicinal Chemistry, 57(12), 4977–5010. https://doi.org/10.1021/JM4004285

Zhang, L., Fourches, D., Sedykh, A., Zhu, H., Golbraikh, A., Ekins, S., … Tropsha, A. (2013). Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening. Journal of Chemical Information and Modeling, 53(2), 475–492. https://doi.org/10.1021/ci300421n

Low, Y., Sedykh, A., Fourches, D., Golbraikh, A., Whelan, M., Rusyn, I., & Tropsha, A. (2013). Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification. Chemical Research in Toxicology, 26(8), 1199–1208. https://doi.org/10.1021/TX400110F

Fourches, D., Muratov, E., Ding, F., Dokholyan, N. V., & Tropsha, A. (2013). Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches. Journal of Chemical Information and Modeling, 53(8), 1915–1922. https://doi.org/10.1021/CI400216Q

Fourches, D., & Tropsha, A. (2013). Using Graph Indices for the Analysis and Comparison of Chemical Datasets. Molecular Informatics, 32(9-10), 827–842. https://doi.org/10.1002/MINF.201300076

Sedykh, A., Fourches, D., Duan, J., Hucke, O., Garneau, M., Zhu, H., … Tropsha, A. (2012). Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions. Pharmaceutical Research, 30(4), 996–1007. https://doi.org/10.1007/S11095-012-0935-X

Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., … Tropsha, A. (2011). Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches. Chemical Research in Toxicology, 24(8), 1251–1262. https://doi.org/10.1021/tx200148a

Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Cherkasov, A., Li, J., … Tetko, I. V. (2010). Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set. Journal of Chemical Information and Modeling, 50(12), 2094–2111. https://doi.org/10.1021/ci100253r

Fourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., & Tropsha, A. (2010). Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species. Chemical Research in Toxicology, 23(1), 171–183. https://doi.org/10.1021/tx900326k

Rodgers, A. D., Zhu, H., Fourches, D., Rusyn, I., & Tropsha, A. (2010). Modeling Liver-Related Adverse Effects of Drugs UsingkNearest Neighbor Quantitative Structure−Activity Relationship Method. Chemical Research in Toxicology, 23(4), 724–732. https://doi.org/10.1021/tx900451r

Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S. Y., Mumper, R. J., & Tropsha, A. (2010). Quantitative Nanostructure−Activity Relationship Modeling. ACS Nano, 4(10), 5703–5712. https://doi.org/10.1021/nn1013484

Fourches, D., Muratov, E., & Tropsha, A. (2010). Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research. Journal of Chemical Information and Modeling, 50(7), 1189–1204. https://doi.org/10.1021/ci100176x

Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatica, P., … Tetko, I. V. (2008). Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis. Journal of Chemical Information and Modeling, 48(4), 766–784. https://doi.org/10.1021/ci700443v

Tetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., … Varnek, A. (2008). Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. Journal of Chemical Information and Modeling, 48(9), 1733–1746. https://doi.org/10.1021/ci800151m

Varnek, A., Fourches, D., Sieffert, N., Solov'ev, V. P., Hill, C., & Lecomte, M. (2007). QSPR Modeling of the AmIII/EuIIISeparation Factor: How Far Can we Predict ? Solvent Extraction and Ion Exchange, 25(1), 1–26. https://doi.org/10.1080/07366290601067481

Varnek, A., Fourches, D., Solov'ev, V., Klimchuk, O., Ouadi, A., & Billard, I. (2007). Successful “In Silico” Design of New Efficient Uranyl Binders. Solvent Extraction and Ion Exchange, 25(4), 433–462. https://doi.org/10.1080/07366290701415820

Tetko, I. V., Solov'ev, V. P., Antonov, A. V., Yao, X., Doucet, J. P., Fan, B., … Varnek, A. (2006). Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure−Property Relationship Studies of Metal Complexation with Ionophores. Journal of Chemical Information and Modeling, 46(2), 808–819. https://doi.org/10.1021/ci0504216

Varnek, A., Fourches, D., Hoonakker, F., & Solov’ev, V. P. (2005). Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. Journal of Computer-Aided Molecular Design, 19(9-10), 693–703. https://doi.org/10.1007/s10822-005-9008-0

Varnek, A., Fourches, D., Solov'e, V. P., Baulin, V. E., Turanov, A. N., Karandashev, V. K., … Katritzky, A. R. (2004). “In Silico” Design of New Uranyl Extractants Based on Phosphoryl-Containing Podands:  QSPR Studies, Generation and Screening of Virtual Combinatorial Library, and Experimental Tests. Journal of Chemical Information and Computer Sciences, 44(4), 1365–1382. https://doi.org/10.1021/ci049976b