QSAR Modeling: Where have you been? Where are you going to?
Open accessAbstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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QSAR based on chemical structural parameters is one dominant and most developed toxicity prediction method.
“One dominant and most developed toxicity prediction method is Quantitative Structure-Activity Relationships (QSAR) based on chemical structural parameters [10].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [10]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works
Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria?
Brogden, K. A. (2005) · Nature Reviews Microbiology · doi:10.1038/nrmicro1098 · Cited by 5,829 · see citing works
Some Factors in the Interpretation of Protein Denaturation
Kauzmann, W. (1959) · Advances in protein chemistry · doi:10.1016/s0065-3233(08)60608-7 · Cited by 4,880 · see citing works
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works
Antibacterial resistance worldwide: causes, challenges and responses
Levy, S. B., & Marshall, B. M. (2004) · Nature Medicine · doi:10.1038/nm1145 · Cited by 4,047 · see citing works
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931 · see citing works
Self-citationGolbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735 · see citing works
Self-citationMechanisms of Antimicrobial Peptide Action and Resistance
Yeaman, M. R., & Yount, N. Y. (2003) · Pharmacological Reviews · doi:10.1124/pr.55.1.2 · Cited by 2,998 · see citing works
Goodford, P. J. (1985) · Journal of Medicinal Chemistry · doi:10.1021/jm00145a002 · Cited by 2,642 · see citing works
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529 · see citing works
<i>p</i>-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01062a035 · Cited by 2,313 · see citing works
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,301 · see citing works
A Reëxamination of the Hammett Equation.
Jaffe, H. H. (1953) · Chemical Reviews · doi:10.1021/cr60165a003 · Cited by 2,132 · see citing works
Best Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,042 · see citing works
Self-citationReoptimization of MDL Keys for Use in Drug Discovery
Durant, J. L., Leland, B. A., Henry, D. R., & Nourse, J. G. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010132r · Cited by 1,886 · see citing works
Molecular Descriptors for Chemoinformatics
Todeschini, R., & Consonni, V. (2009) · Methods and principles in medicinal chemistry · doi:10.1002/9783527628766 · Cited by 1,881 · see citing works
Self-citationKlebe, G., Abraham, U., & Mietzner, T. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00050a010 · Cited by 1,822 · see citing works
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667 · see citing works
A New Substituent Constant, π, Derived from Partition Coefficients
Fujita, T., Iwasa, J., & Hansch, C. H. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01077a028 · Cited by 1,371 · see citing works
Amphipathic, α-helical antimicrobial peptides
Tossi, A., Sandri, L., & Giangaspero, A. (2000) · Biopolymers · doi:10.1002/1097-0282(2000)55:1 · Cited by 1,245 · see citing works
Rendić, S., & Carlo, F. J. D. (1997) · Drug Metabolism Reviews · doi:10.3109/03602539709037591 · Cited by 1,165 · see citing works
Caruana, R. (1998) · Cited by 1,110 · see citing works
HANSCH, C., MALONEY, P. P., FUJITA, T., & MUIR, R. M. (1962) · Nature · doi:10.1038/194178b0 · Cited by 1,056 · see citing works
The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals
Dix, D. J., Houck, K. A., Martin, M. T., Richard, A. M., Setzer, R. W., & Kavlock, R. J. (2006) · Toxicological Sciences · doi:10.1093/toxsci/kfl103 · Cited by 1,013 · see citing works
Self-citationZur Theorie der Alkoholnarkose
Meyer, H. (1899) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/bf01834479 · Cited by 1,007 · see citing works
Cationic peptides: effectors in innate immunity and novel antimicrobials
Hancock, R. E. (2001) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(01)00092-5 · Cited by 978 · see citing works
The relationship between peptide structure and antibacterial activity
Powers, J.-P. S., & Hancock, R. E. W. (2003) · Peptides · doi:10.1016/j.peptides.2003.08.023 · Cited by 947 · see citing works
Safety assessment for nanotechnology and nanomedicine: concepts of nanotoxicology
Oberdörster, G. (2009) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2009.02187.x · Cited by 943 · see citing works
Quantitative approach to biochemical structure-activity relationships
Hansch, C. H. (1969) · Accounts of Chemical Research · doi:10.1021/ar50020a002 · Cited by 921 · see citing works
Large-scale prediction and testing of drug activity on side-effect targets
Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 880 · see citing works
Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M., Gramatica, P., Jaworska, J. S., Kahn, S. D., Klopman, G., Marchant, C. A., et al. (2005) · Alternatives to Laboratory Animals · doi:10.1177/026119290503300209 · Cited by 840 · see citing works
Self-citationAutomated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832 · see citing works
Fourches, D., Muratov, E., & Tropsha, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100176x · Cited by 832 · see citing works
Self-citationSummary of information on human CYP enzymes: human P450 metabolism data
Rendić, S. (2002) · Drug Metabolism Reviews · doi:10.1081/dmr-120001392 · Cited by 819 · see citing works
Learning Multiple Tasks with Kernel Methods
Evgeniou, T., Micchelli, C. A., & Pontil, M. (2005) · doi:10.5555/1046920.1088693 · Cited by 807 · see citing works
Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles
Puzyn, T., Rasulev, B., Gajewicz, A., Hu, X., Dasari, T. P., Michalková, A., Hwang, H., Toropov, A. A., Leszczyńska, D., & Leszczyński, J. (2011) · Nature Nanotechnology · doi:10.1038/nnano.2011.10 · Cited by 786 · see citing works
Chirico, N., & Gramatica, P. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200211n · Cited by 783 · see citing works
Self-citationSteric Effects in Organic Chemistry.
Lewis, E. S. (1957) · Journal of the American Chemical Society · doi:10.1021/ja01561a080 · Cited by 780 · see citing works
On Outliers and Activity CliffsWhy QSAR Often Disappoints
Maggiora, G. M. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060117s · Cited by 728 · see citing works
Guengerich, F. P. (1995) · Springer eBooks · doi:10.1007/978-1-4757-2391-5_14 · Cited by 727 · see citing works
QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review
Jaworska, J. S., Nikolova-Jeliazkova, N., & Aldenberg, T. (2005) · Alternatives to Laboratory Animals · doi:10.1177/026119290503300508 · Cited by 685 · see citing works
PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching
Dixon, S. L., Smondyrev, A. M., & Rao, S. N. (2006) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2006.00384.x · Cited by 676 · see citing works
Chance factors in studies of quantitative structure-activity relationships
Topliss, J. G., & Edwards, R. P. (1979) · Journal of Medicinal Chemistry · doi:10.1021/jm00196a017 · Cited by 675 · see citing works
Molecular Similarity in Medicinal Chemistry
Maggiora, G. M., Vogt, M., Stumpfe, D., & Bajorath, J. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm401411z · Cited by 669 · see citing works
Giangaspero, A., Sandri, L., & Tossi, A. (2001) · European Journal of Biochemistry · doi:10.1046/j.1432-1033.2001.02494.x · Cited by 650 · see citing works
Judson, R., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M., et al. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901392 · Cited by 610 · see citing works
Self-citationByvatov, E., Fechner, U., Sadowski, J., & Schneider, G. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0341161 · Cited by 569 · see citing works
Peptide quantitative structure-activity relationships, a multivariate approach
Hellberg, S., SJOESTROEM, M., Skagerberg, B., & Wold, S. (1987) · Journal of Medicinal Chemistry · doi:10.1021/jm00390a003 · Cited by 541 · see citing works
Hansch, C. H., Muir, R. M., Fujita, T., Maloney, P. P., Geiger, F., & Streich, M. (1963) · Journal of the American Chemical Society · doi:10.1021/ja00901a033 · Cited by 513 · see citing works
MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist
Cruciani, G., Carosati, E., Boeck, B. D., Ethirajulu, K., Mackie, C. E., Howe, T. J., & Vianello, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050529c · Cited by 497 · see citing works
Kavlock, R. J., Chandler, K. J., Houck, K. A., Hunter, S., Judson, R., Kleinstreuer, N., Knudsen, T. B., Martin, M. T., Padilla, S., Reif, D. M., et al. (2012) · Chemical Research in Toxicology · doi:10.1021/tx3000939 · Cited by 496 · see citing works
Self-citationCan We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484 · see citing works
Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening
Tropsha, A., & Golbraikh, A. (2007) · Current Pharmaceutical Design · doi:10.2174/138161207782794257 · Cited by 479 · see citing works
Self-citationON THE MECHANISM OF INTESTINAL ABSORPTION OF DRUGS
Hogben, C. A. M., Tocco, D. J., Brodie, B. B., & Schanker, L. S. (1959) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1016/s0022-3565(25)12995-9 · Cited by 476 · see citing works
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468 · see citing works
Self-citationPastor, M., Cruciani, G., McLay, I. M., Pickett, S. D., & Clementi, S. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000941m · Cited by 466 · see citing works
Utilization of operational schemes for analog synthesis in drug design
Topliss, J. G. (1972) · Journal of Medicinal Chemistry · doi:10.1021/jm00280a002 · Cited by 458 · see citing works
Zelezetsky, I., & Tossi, A. (2006) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2006.03.021 · Cited by 439 · see citing works
Klopman, G. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00336a004 · Cited by 426 · see citing works
Tetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., Öberg, T., Todeschini, R., Fourches, D., & Varnek, A. A. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800151m · Cited by 423 · see citing works
Self-citationThe Partition of Organic Compounds Between Higher Alcohols and Water.
Collander, R., Lindholm, M., Haug, C. M., Stene, J., & Sörensen, N. A. (1951) · Acta chemica Scandinavica/Acta chemica Scandinavica. B, Organic chemistry and biochemistry/Acta chemica Scandinavica. A, Physical and inorganic chemistry/Acta chemica Scandinavica. Series B. Organic chemistry and biochemistry/Acta chemica Scandinavica. Series A, Physical and inorganic chemistry · doi:10.3891/acta.chem.scand.05-0774 · Cited by 412 · see citing works
Mixture toxicity and its modeling by quantitative structure-activity relationships
Altenburger, R., Nendza, M., & Schüürmann, G. (2003) · Environmental Toxicology and Chemistry · doi:10.1897/01-386 · Cited by 400 · see citing works
Quantitative Nanostructure−Activity Relationship Modeling
Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S. Y., Mumper, R. J., & Tropsha, A. (2010) · ACS Nano · doi:10.1021/nn1013484 · Cited by 388 · see citing works
Self-citationQSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction
Lagunin, A. A., Zakharov, A. V., Filimonov, D. A., & Poroikov, V. (2011) · Molecular Informatics · doi:10.1002/minf.201000151 · Cited by 380 · see citing works
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376 · see citing works
High-throughput generation of small antibacterial peptides with improved activity
Hilpert, K., Volkmer, R., Walter, T., & Hancock, R. E. W. (2005) · Nature Biotechnology · doi:10.1038/nbt1113 · Cited by 373 · see citing works
Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
Guha, R., & Drie, J. H. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7004093 · Cited by 361 · see citing works
Development and Application of New Steric Substituent Parameters in Drug Design
Verloop, A., Hoogenstraaten, W., & Tipker, J. (1976) · Elsevier eBooks · doi:10.1016/b978-0-12-060307-7.50010-9 · Cited by 360 · see citing works
Cherkasov, A. R., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C., Volkmer, R., & Hancock, R. E. W. (2008) · ACS Chemical Biology · doi:10.1021/cb800240j · Cited by 356 · see citing works
Self-citationNanomedicines and nanotoxicology: some physiological principles
Garnett, M. C., & Kallinteri, P. (2006) · Occupational Medicine · doi:10.1093/occmed/kql052 · Cited by 352 · see citing works
Cronin, M., & Schultz, T. W. (2003) · Journal of Molecular Structure THEOCHEM · doi:10.1016/s0166-1280(02)00616-4 · Cited by 350 · see citing works
Self-citationVirtual Screening in Drug Discovery - A Computational Perspective
Reddy, A. S., Pati, S. P., Kumar, P. P., Pradeep, H., & Sastry, G. N. (2007) · Current Protein and Peptide Science · doi:10.2174/138920307781369427 · Cited by 350 · see citing works
Marchant, C. A., Briggs, K. A., & Long, A. (2008) · Toxicology Mechanisms and Methods · doi:10.1080/15376510701857320 · Cited by 344 · see citing works
A Predictive Toxicological Paradigm for the Safety Assessment of Nanomaterials
Meng, H., Xia, T., George, S., & Nel, A. E. (2009) · ACS Nano · doi:10.1021/nn9005973 · Cited by 344 · see citing works
A Modular Approach to the ECVAM Principles on Test Validity
Hartung, T., Bremer, S., Casati, S., Coecke, S., Corvi, R., Fortaner, S., Gribaldo, L., Halder, M., Hoffmann, S., Roi, A. J., et al. (2004) · Alternatives to Laboratory Animals · doi:10.1177/026119290403200503 · Cited by 341 · see citing works
Hose — a novel substructure code
Bremser, W. (1978) · Analytica Chimica Acta · doi:10.1016/s0003-2670(01)83100-7 · Cited by 325 · see citing works
Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0498719 · Cited by 322 · see citing works
Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
Martin, T. M., Harten, P. F., Young, D. M., Muratov, E. N., Golbraikh, A., Zhu, H., & Tropsha, A. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300338w · Cited by 321 · see citing works
Self-citationKoczulla, A. R., & Bals, R. (2003) · Drugs · doi:10.2165/00003495-200363040-00005 · Cited by 309 · see citing works
Some New Trends in Chemical Graph Theory
García‐Domenech, R., Gálvez, J., de Julián‐Ortiz, J. V., & Pogliani, L. (2008) · Chemical Reviews · doi:10.1021/cr0780006 · Cited by 302 · see citing works
Calculation of hydrophobic constant (log P) from .pi. and f constants
Leo, A. J., Jow, P. Y. C., Silipo, C., & Hänsch, C. (1975) · Journal of Medicinal Chemistry · doi:10.1021/jm00243a001 · Cited by 298 · see citing works
Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning
Fjell, C. D., Jenssen, H., Hilpert, K., Cheung, W., Panté, N., Hancock, R. E. W., & Cherkasov, A. R. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm8015365 · Cited by 297 · see citing works
Self-citationCan innate immunity be enhanced to treat microbial infections?
Finlay, B. B., & Hancock, R. E. W. (2004) · Nature Reviews Microbiology · doi:10.1038/nrmicro908 · Cited by 296 · see citing works
Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms
Kirchmair, J., Williamson, M. J., Tyzack, J. D., Tan, L., Bond, P. J., Bender, A., & Glen, R. C. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200542m · Cited by 295 · see citing works
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
Zhu, H., Tropsha, A., Fourches, D., Varnek, A. A., Papa, E., Gramatica, P., Öberg, T., Dao, P., Cherkasov, A. R., & Tetko, I. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700443v · Cited by 288 · see citing works
Self-citationOn the Development and Validation of QSAR Models
Gramatica, P. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-059-5_21 · Cited by 282 · see citing works
Self-citationLeeson, P. D. (2012) · Nature · doi:10.1038/481455a · Cited by 269 · see citing works
Chemoinformatics in Drug Discovery
(2005) · Methods and principles in medicinal chemistry · doi:10.1002/3527603743 · Cited by 268 · see citing works
Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis
Ortíz, Á. R., Pisabarro, M. T., Gago, F., & Wade, R. C. (1995) · Journal of Medicinal Chemistry · doi:10.1021/jm00014a020 · Cited by 267 · see citing works
Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model
Torrent, M., Andreu, D., Nogués, M. V., & Boix, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016968 · Cited by 261 · see citing works
Reactions and enzymes in the metabolism of drugs and other xenobiotics
Testa, B., Pedretti, A., & Vistoli, G. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.01.017 · Cited by 254 · see citing works
Toward the Development of “Nano‐QSARs”: Advances and Challenges
Puzyn, T., Leszczyńska, D., & Leszczyński, J. (2009) · Small · doi:10.1002/smll.200900179 · Cited by 247 · see citing works
Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment
Kavlock, R. J., Austin, C. P., & Tice, R. R. (2008) · Risk Analysis · doi:10.1111/j.1539-6924.2008.01168.x · Cited by 239 · see citing works
Craig, P. N. (1971) · Journal of Medicinal Chemistry · doi:10.1021/jm00290a004 · Cited by 229 · see citing works
Nature of the ortho effect. II. Composition of the Taft steric parameters
Charton, M. (1969) · Journal of the American Chemical Society · doi:10.1021/ja01031a016 · Cited by 225 · see citing works
Zhao, Y., Xing, G., & Chai, Z. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.77 · Cited by 220 · see citing works
Heister, E., Lamprecht, C., Neves, V., Tîlmaciu, C., Datas, L., Emmanuel Flahaut, B., Soula, B., Hinterdorfer, P., Coley, H. M., Silva, S. R. P., et al. (2010) · ACS Nano · doi:10.1021/nn100069k · Cited by 219 · see citing works
Gedeck, P., Rohde, B., & Bartels, C. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050413p · Cited by 219 · see citing works
Chem-Bioinformatics: Comparative QSAR at the Interface between Chemistry and Biology
Hansch, C. H., Hoekman, D., Leo, A. J., Weininger, D., & Selassie, C. D. (2002) · Chemical Reviews · doi:10.1021/cr0102009 · Cited by 218 · see citing works
ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors
Varnek, A. A., Fourches, D., Horvath, D., Klimchuk, O., Gaudin, C., Vayer, P., Solov’ev, V. P., Hoonakker, F., Tetko, I. V., & Marcou, G. (2008) · Current Computer - Aided Drug Design · doi:10.2174/157340908785747465 · Cited by 213 · see citing works
Self-citationRecent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry
Stumpfe, D., Hu, Y., Dimova, D., & Bajorath, J. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm401120g · Cited by 208 · see citing works
Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation
Ajmani, S., Jadhav, K., & Kulkarni, S. A. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0501286 · Cited by 203 · see citing works
Are the Chemical Structures in Your QSAR Correct?
Young, D., Martin, T. M., Venkatapathy, R., & Harten, P. F. (2008) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810084 · Cited by 196 · see citing works
Generalized molecular descriptors
Randić, M. (1991) · Journal of Mathematical Chemistry · doi:10.1007/bf01200821 · Cited by 195 · see citing works
Kubinyi, H. (1977) · Journal of Medicinal Chemistry · doi:10.1021/jm00215a002 · Cited by 195 · see citing works
Nanotoxicology and nanomedicine: making hard decisions
Linkov, I., Satterstrom, F. K., & Corey, L. M. (2008) · Nanomedicine Nanotechnology Biology and Medicine · doi:10.1016/j.nano.2008.01.001 · Cited by 192 · see citing works
The expanding role of quantitative structure-activity relationships (QSAR) in toxicology
Hansch, C. H., Hoekman, D. A., Leo, A. J., Zhang, L., & Li, P. (1995) · Toxicology Letters · doi:10.1016/0378-4274(95)03356-p · Cited by 188 · see citing works
Stone, V., & Donaldson, K. (2006) · Nature Nanotechnology · doi:10.1038/nnano.2006.69 · Cited by 184 · see citing works
Steric parameters in drug design. Monoamine oxidase inhibitors and antihistamines
Kutter, E., & Hansch, C. H. (1969) · Journal of Medicinal Chemistry · doi:10.1021/jm00304a020 · Cited by 178 · see citing works
Distributed structure-searchable toxicity (DSSTox) public database network: a proposal
Richard, A. M., & Williams-DeVane, C. (2002) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/s0027-5107(01)00289-5 · Cited by 170 · see citing works
Self-citationMETA. 1. A Program for the Evaluation of Metabolic Transformation of Chemicals
Klopman, G., Dimayuga, M., & Talafous, J. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00022a014 · Cited by 169 · see citing works
Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening1
Martin, M. T., Knudsen, T. B., Reif, D. M., Houck, K. A., Judson, R., Kavlock, R. J., & Dix, D. J. (2011) · Biology of Reproduction · doi:10.1095/biolreprod.111.090977 · Cited by 164 · see citing works
Three new consensus QSAR models for the prediction of Ames genotoxicity
Votano, J. R. (2004) · Mutagenesis · doi:10.1093/mutage/geh043 · Cited by 160 · see citing works
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
Self-citationThe SMARTCyp cytochrome P450 metabolism prediction server
Rydberg, P., Gloriam, D. E. I., & Olsen, L. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq584 · Cited by 153 · see citing works
Golbraikh, A., Muratov, E., Fourches, D., & Tropsha, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400572x · Cited by 152 · see citing works
Self-citationStrategy in drug design. Cluster anlysis as an aid in the selection of substituents
Hansch, C. H., Unger, S., & Forsythe, A. B. (1973) · Journal of Medicinal Chemistry · doi:10.1021/jm00269a004 · Cited by 150 · see citing works
Thomas, R. S., Black, M. B., Li, L., Healy, E., Chu, T., Bao, W., Andersen, M. E., & Wolfinger, R. D. (2012) · Toxicological Sciences · doi:10.1093/toxsci/kfs159 · Cited by 148 · see citing works
Computational Medicinal Chemistry for Drug Discovery
Matter, H. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03071-5 · Cited by 143 · see citing works
Nanoparticles reconstruct lipids
Dawson, K. A., Salvati, A. L., & Lynch, I. (2009) · Nature Nanotechnology · doi:10.1038/nnano.2008.426 · Cited by 140 · see citing works
Predicting Drug Metabolism: A Site of Metabolism Prediction Tool Applied to the Cytochrome P450 2C9
Zamora, I., Afzelius, L., & Cruciani, G. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm021104i · Cited by 138 · see citing works
Oprea, T. I., Tropsha, A., Faulon, J., & Rintoul, M. D. (2007) · Nature Chemical Biology · doi:10.1038/nchembio0807-447 · Cited by 138 · see citing works
Self-citationDoweyko, A. M. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-4068-0 · Cited by 135 · see citing works
Mekenyan, O. G., Dimitrov, S. D., Pavlov, T. S., & Veith, G. D. (2004) · Current Pharmaceutical Design · doi:10.2174/1381612043452596 · Cited by 135 · see citing works
Toward A Quantitative Comparative Toxicology of Organic Compounds
Hansch, C. H., Kim, D., Leo, A. J., Novellino, E., Silipo, C., Vittoria, A., Bond, J. A., & Henderson, R. F. (1989) · Critical Reviews in Toxicology · doi:10.3109/10408448909037471 · Cited by 134 · 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
Self-citationCRAMER, R. D. I., Jilek, R. J., Güssregen, S., Clark, S. J., Wendt, B., & Clark, R. D. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm049501b · Cited by 130 · see citing works
Self-citationNanoParticle Ontology for cancer nanotechnology research
Thomas, D., Pappu, R. V., & Baker, N. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.03.001 · Cited by 129 · see citing works
The permeability of plant protoplasts to non-electrolytes
Collander, R. (1937) · Transactions of the Faraday Society · doi:10.1039/tf9373300985 · Cited by 127 · see citing works
Guidelines for developing and using quantitative structure-activity relationships
Walker, J. D., Jaworska, J. S., Comber, M. H. I., Schultz, T. W., & Dearden, J. C. (2003) · Environmental Toxicology and Chemistry · doi:10.1897/01-627 · Cited by 125 · see citing works
Self-citationGohlke, H., & Klebe, G. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020808p · Cited by 122 · see citing works
Sedykh, A., Zhu, H., Tang, H., Zhang, L., Richard, A., Rusyn, I., & Tropsha, A. (2011) · Environmental Health Perspectives · doi:10.1289/ehp.1002476 · Cited by 119 · see citing works
Self-citationQSAR Comparative Study of Wiener Descriptors for Weighted Molecular Graphs
Ivanciuc, O. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000068y · Cited by 118 · see citing works
WOMBAT: World of Molecular Bioactivity
Olah, M., Mracec, M., Halip, L., Rad-Curpăn, R., Bora, A., Hadaruga, N. G., Olah, I., Banda, M., Simon, Z., Mracec, M., et al. (2005) · Methods and principles in medicinal chemistry · doi:10.1002/3527603743.ch9 · Cited by 117 · see citing works
Exploiting PubChem for virtual screening
Xie, X.-Q. S. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.524924 · Cited by 116 · see citing works
The Relation of Chemical Structure to Bio-Logical Activity in Growth Substances
Veldstra, H. (1953) · Annual Review of Plant Physiology · doi:10.1146/annurev.pp.04.060153.001055 · Cited by 114 · see citing works
Doweyko, A. M. (1988) · Journal of Medicinal Chemistry · doi:10.1021/jm00402a025 · Cited by 113 · see citing works
Benigni, R., Netzeva, T. I., Benfenati, E., Bossa, C., Franke, R., Helma, C., Hulzebos, E., Marchant, C. A., RICHARD, A., Woo, Y.-T., et al. (2007) · Journal of Environmental Science and Health Part C · doi:10.1080/10590500701201828 · Cited by 109 · see citing works
Self-citationWOMBAT and WOMBAT‐PK: Bioactivity Databases for Lead and Drug Discovery
Olah, M., Rad-Curpăn, R., Ostopovici, L., Bora, A., Hadaruga, N. G., Hadaruga, D. I., Moldovan, R., Fuliaş, A., Mractc, M., & Oprea, T. I. (2007) · Chemical biology · doi:10.1002/9783527619375.ch13b · Cited by 105 · see citing works
A quality alert and call for improved curation of public chemistry databases
Williams, A. J., & Ekins, S. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.07.007 · Cited by 104 · see citing works
Reaction Site Mapping of Xenobiotic Biotransformations
Boyer, S. K., Arnby, C. H., Carlsson, L., Smith, J., Stein, V., & Glen, R. C. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600376q · Cited by 103 · see citing works
Practical Considerations on the Use of Predictive Models for Regulatory Purposes
Tunkel, J., Mayo, K., Austin, C., Hickerson, A., & Howard, P. H. (2005) · Environmental Science & Technology · doi:10.1021/es049220t · Cited by 102 · see citing works
Using chemical categories to fill data gaps in hazard assessment
van Leeuwen, K., Schultz, T. W., Henry, T., Diderich, B., & Veith, G. D. (2009) · SAR and QSAR in environmental research · doi:10.1080/10629360902949179 · Cited by 102 · see citing works
Frye, S. V., Crosby, M., Edwards, T., & Juliano, R. L. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3462 · Cited by 101 · see citing works
Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening
Zhang, L., Fourches, D., Sedykh, A., Zhu, H., Golbraikh, A., Ekins, S., Clark, J. A., Connelly, M., Sigal, M. S., Hodges, D., et al. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300421n · Cited by 101 · see citing works
Self-citationAnalysis of a Large Structure‐Activity Data Set Using Recursive Partitioning
Hawkins, D. M., Young, S. S., & Rusinko, A. (1997) · Quantitative Structure-Activity Relationships · doi:10.1002/qsar.19970160404 · Cited by 99 · see citing works
ISA-TAB-Nano: A Specification for Sharing Nanomaterial Research Data in Spreadsheet-based Format
Thomas, D., Gaheen, S., Harper, S. L., Fritts, M. J., Klaessig, F., Hahn-Dantona, E. A., Paik, D. S., Pan, S., Stafford, G. A., Freund, E. T., et al. (2013) · BMC Biotechnology · doi:10.1186/1472-6750-13-2 · Cited by 99 · see citing works
QSAR modeling and computer‐aided design of antimicrobial peptides
Jenssen, H., Fjell, C. D., Cherkasov, A. R., & Hancock, R. E. W. (2007) · Journal of Peptide Science · doi:10.1002/psc.908 · Cited by 99 · see citing works
Self-citationOn the interpretation and interpretability of quantitative structure–activity relationship models
Guha, R. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9240-5 · Cited by 98 · see citing works
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Erhan, D., L'Heureux, P., Yue, S. Y., & Bengio, Y. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050367t · Cited by 98 · see citing works
Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance
Guha, R., & Jurs, P. C. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050022a · Cited by 98 · see citing works
Pharmaceuticals in the environment: Good practice in predicting acute ecotoxicological effects
Madden, J. C., Enoch, S. J., Hewitt, M., & Cronin, M. (2008) · Toxicology Letters · doi:10.1016/j.toxlet.2008.12.005 · Cited by 97 · see citing works
Self-citationOptimization of Antibacterial Peptides by Genetic Algorithms and Cheminformatics
Fjell, C. D., Jenssen, H., Cheung, W., Hancock, R. E. W., & Cherkasov, A. R. (2010) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2010.01044.x · Cited by 96 · see citing works
Self-citationKuz’min, V. Е., Artemenko, A. G., LOZYTSKA, R., Fedtchouk, A. S., Lozitsky, V. P., Muratov, E. N., & Mescheriakov, A. K. (2005) · SAR and QSAR in environmental research · doi:10.1080/10659360500037206 · Cited by 93 · see citing works
Self-citationReceptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions
Polański, J. (2009) · Current Medicinal Chemistry · doi:10.2174/092986709788803286 · Cited by 91 · see citing works
Zhu, H., Rusyn, I., Richard, A., & Tropsha, A. (2008) · Environmental Health Perspectives · doi:10.1289/ehp.10573 · Cited by 91 · see citing works
Self-citationVarnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86 · see citing works
Self-citationIssues in representation of molecular structure
Hall, L. H., & Kier, L. B. (2001) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00097-3 · Cited by 85 · see citing works
Carlsson, L., Spjuth, O., Adams, S., Glen, R. C., & Boyer, S. K. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-362 · Cited by 85 · see citing works
Topomers: A Validated Protocol for Their Self-Consistent Generation
Jilek, R. J., & CRAMER, R. D. I. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049961d · Cited by 85 · see citing works
Self-citationInterpretation 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
Self-citationGroup‐Based QSAR (G‐QSAR): Mitigating Interpretation Challenges in QSAR
Ajmani, S., Jadhav, K., & Kulkarni, S. A. (2008) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810063 · Cited by 82 · see citing works
A computer automated structure evaluation (CASE) approach to calculation of partition coefficient
Klopman, G., & Wang, S. (1991) · Journal of Computational Chemistry · doi:10.1002/jcc.540120815 · Cited by 78 · see citing works
Toxicity Testing in the 21st Century
Greenberg, M., & Lowrie, K. W. (2009) · Risk Analysis · doi:10.1111/j.1539-6924.2009.01222.x · 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
Self-citationKuz’min, V. Е., Artemenko, A. G., Muratov, E. N., VOLINECKAYA, I., Makarov, V., Riabova, O., Wutzler, P., & Schmidtke, M. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm0704806 · Cited by 71 · see citing works
Self-citationLoerzel, V. W., & Conner, N. E. (2014) · American Journal of Hospice and Palliative Medicine® · doi:10.1177/1049909114549182 · Cited by 71 · see citing works
Structure and Association of Human Lactoferrin Peptides withEscherichia coliLipopolysaccharide
Chapple, D. S., Hussain, R., Joannou, C. L., Hancock, R. E. W., Odell, E. W., Evans, R. W., & Siligardi, G. (2004) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.48.6.2190-2198.2004 · Cited by 71 · see citing works
Chandler, K. J., Barrier, M., Jeffay, S. C., Nichols, H. P., Kleinstreuer, N., Singh, A. V., Reif, D. M., Sipes, N. S., Judson, R., Dix, D. J., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018540 · Cited by 70 · see citing works
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CRAMER, R. D. I. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9495-0 · Cited by 70 · see citing works
Self-citationOprisiu, 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
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Olah, M., Bologa, C., & Oprea, T. I. (2004) · Current Drug Discovery Technologies · doi:10.2174/1570163043334965 · Cited by 69 · see citing works
Tresadern, G., Bemporad, D., & Howe, T. J. (2009) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2009.01.003 · Cited by 67 · see citing works
Guha, R., Stanton, D. T., & Jurs, P. C. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050110v · Cited by 67 · see citing works
Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing
Small, B. G., McColl, B. W., Allmendinger, R., Pahle, J., López‐Castejón, G., Rothwell, N. J., Knowles, J. D., Mendes, P., Brough, D., & Kell, D. B. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.689 · Cited by 67 · see citing works
Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data
Carlsson, L., Helgee, E. A., & Boyer, S. K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9002206 · Cited by 65 · see citing works
Southan, C., Várkonyi, P., & Mureşan, S. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-10 · Cited by 65 · see citing works
Application of QSPR to Mixtures
Ajmani, S., Rogers, S. C., Barley, M. H., & Livingstone, D. J. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050559o · Cited by 65 · see citing works
Martin, Y. C., Holland, J. B., Jarboe, C. H., & Plotnikoff, N. P. (1974) · Journal of Medicinal Chemistry · doi:10.1021/jm00250a008 · Cited by 64 · see citing works
Self-citation<i>Per Aspera Ad Astra</i> : Application of Simplex Qsar Approach in Antiviral Research
Muratov, E. N., Artemenko, A. G., Varlamova, E. V., Polischuk, P. G., Lozitsky, V. P., Fedchuk, A. S., Lozitska, R. L., Gridina, T. L., Koroleva, L. S., Sil’nikov, V. N., et al. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.194 · Cited by 64 · see citing works
Self-citationJenssen, H., Lejon, T., Hilpert, K., Fjell, C. D., Cherkasov, A. R., & Hancock, R. E. W. (2007) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2007.00543.x · Cited by 63 · see citing works
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Guha, R., & Jurs, P. C. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0499469 · Cited by 62 · see citing works
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Hamilton-Miller, J. M. T. (2004) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2003.12.001 · Cited by 61 · see citing works
Durán, Á., Zamora, I., & Pastor, M. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900228x · Cited by 61 · see citing works
Yaffe, D., Cohen, Y., Espinosa, G., Arenas, À., & Giralt, F. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010323u · Cited by 60 · see citing works
CRAMER, R. D. I., Cruz, P., Stahl, G., Curtiss, W. C., Campbell, B., Masek, B. B., & Soltanshahi, F. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001556 · Cited by 59 · see citing works
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Cherkasov, A. R. (2005) · Current Computer - Aided Drug Design · doi:10.2174/1573409052952288 · Cited by 58 · see citing works
Self-citationNan, Y. H., Bang, J., & Shin, S. Y. (2009) · Peptides · doi:10.1016/j.peptides.2009.01.015 · Cited by 58 · see citing works
Cherkasov, A. R., & Janković, B. (2004) · Molecules · doi:10.3390/91201034 · Cited by 57 · see citing works
Self-citationInterpreting linear support vector machine models with heat map molecule coloring
Rosenbaum, L., Hinselmann, G., Jahn, A., & Zell, A. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-11 · Cited by 57 · see citing works
An approach to the interpretation of backpropagation neural network models in QSAR studies
Baskin, I. I., Айт, А. О., Halberstam, N. M., Palyulin, V. A., & Зефиров, Н. С. (2002) · SAR and QSAR in environmental research · doi:10.1080/10629360290002073 · Cited by 55 · see citing works
Self-citationBerellini, G., Cruciani, G., & Mannhold, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049024x · Cited by 55 · see citing works
Sánchez-Gómez, S., Japelj, B., Jerala, R., Moriyón, I., Alonso, M. F., Leiva, J., Blondelle, S. E., Andrä, J., Brandenburg, K., Lohner, K., et al. (2010) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00904-10 · Cited by 55 · see citing works
Bhonsle, J. B., Venugopal, D., Huddler, D. P., Magill, A. J., & Hicks, R. P. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm070884y · Cited by 54 · see citing works
Conlon, J. M., Ahmed, E., & Condamine, E. (2009) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2009.00882.x · Cited by 54 · see citing works
The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study
Kuz’min, V. Е., Muratov, E. N., Artemenko, A. G., Gorb, L., Qasim, M., & Leszczyński, J. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9211-x · Cited by 54 · see citing works
Self-citationHajjo, R., Setola, V., Roth, B. L., & Tropsha, A. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm2011657 · Cited by 53 · see citing works
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- [77]Flynn GL Gerrity TR Henry CJ Physicochemical Determinants of Skin Absorption Principles of Route-to- Route Extrapolation for Risk assessment 1990 Elsevier New York, NY 93 117
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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
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Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 793 · see citing works
Role of computer-aided drug design in modern drug discovery
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Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718 · see citing works
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Neves, B. J., Braga, R. C., Melo-Filho, C. C., Moreira‐Filho, J. T., Muratov, E. N., & Andrade, C. H. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01275 · Cited by 502 · see citing works
Self-citationConvolutional embedding of attributed molecular graphs for physical property prediction
Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470 · see citing works
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · 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
Self-citationPatil, P. C., Kaleem, M., Mishra, R., Ray, S., Ahmad, A., Verma, D., Bhayye, S. S., Dubey, R., Singh, H. N., & Kumar, S. (2024) · Biomedicines · doi:10.3390/biomedicines12010201 · Cited by 429 · see citing works
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 423 · see citing works
Self-citationPredictive and mechanistic multivariate linear regression models for reaction development
Santiago, C. B., Guo, J.-Y., & Sigman, M. S. (2018) · Chemical Science · doi:10.1039/c7sc04679k · Cited by 403 · see citing works
Descriptors and their selection methods in QSAR analysis: paradigm for drug design
Danishuddin, M., & Khan, A. U. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.06.013 · Cited by 394 · see citing works
Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
Walters, W. P., & Barzilay, R. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00699 · Cited by 394 · see citing works
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 367 · see citing works
Autonomous Discovery in the Chemical Sciences Part I: Progress
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 365 · see citing works
Koelmans, A. A., Redondo‐Hasselerharm, P. E., Nor, N. H. M., & Kooi, M. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c02982 · Cited by 351 · see citing works
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R., & Coley, C. W. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00502 · Cited by 349 · see citing works
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Dobbelaere, M. R., Plehiers, P. P., de Vijver, R. V., Stevens, C. V., & Geem, K. M. V. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 339 · see citing works
Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation
Decherchi, S., & Cavalli, A. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00534 · Cited by 301 · see citing works
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
Self-citationTrust, but Verify II: A Practical Guide to Chemogenomics Data Curation
Fourches, D., Muratov, E., & Tropsha, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00129 · Cited by 294 · see citing works
Self-citationComputational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches
Subramanian, G., Ramsundar, B., Pande, V. S., & Denny, R. A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00290 · Cited by 292 · see citing works
The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery
Dzobo, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00041-4 · Cited by 285 · see citing works
Li, S., Wu, Y., Zheng, H., Li, H., Zheng, Y., Nan, J., Ma, J., Nagarajan, D., & Chang, J. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.136977 · Cited by 275 · see citing works
vNN Web Server for ADMET Predictions
Schyman, P., Liu, R., Desai, V., & Wallqvist, A. (2017) · Frontiers in Pharmacology · doi:10.3389/fphar.2017.00889 · Cited by 264 · see citing works
Gramatica, P. (2020) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.20200701.oa1 · Cited by 263 · see citing works
Self-citationFP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
Cai, H., Zhang, H., Zhao, D., Wu, J., & Wang, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac408 · Cited by 257 · see citing works
The transformational role of GPU computing and deep learning in drug discovery
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00463-x · Cited by 255 · see citing works
Self-citationIn silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
Neto, L. R. D. S., Moreira‐Filho, J. T., Neves, B. J., Maidana, R. L. B. R., Guimarães, A. C. R., Furnham, N., Andrade, C. H., & Silva, F. P. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00093 · Cited by 243 · see citing works
In silico toxicology protocols
Myatt, G. J., Ahlberg, E., Akahori, Y., Allen, D., Amberg, A., Anger, L. T., Aptula, A., Auerbach, S., Beilke, L., Bellion, P., et al. (2018) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2018.04.014 · Cited by 240 · see citing works
Self-citationMolecular modelling of compounds used for corrosion inhibition studies: a review
Ebenso, E. E., Verma, C., Olasunkanmi, L. O., Akpan, E. D., Verma, D. K., Lgaz, H., Guo, L., Kaya, S., & Quraishi, M. A. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp00244a · Cited by 237 · 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
A renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235 · see citing works
Self-citationAccelerating antibiotic discovery through artificial intelligence
Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234 · see citing works
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
Soleimany, A. P., Amini, A., Goldman, S., Rus, D. L., Bhatia, S. N., & Coley, C. W. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00546 · Cited by 231 · see citing works
Ocaña, A., Pandiella, A., Privat, C., Bravo, I., Luengo-Oroz, M. A., Amir, E., & Győrffy, B. (2025) · Biomarker Research · doi:10.1186/s40364-025-00758-2 · Cited by 228 · see citing works
Ahn, S., Hong, M., Sundararajan, M., Ess, D. H., & Baik, M. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00073 · Cited by 224 · see citing works
Toward Good Read-Across Practice (GRAP) guidance
Ball, N. S. (2016) · ALTEX · doi:10.14573/altex.1601251 · Cited by 218 · see citing works
Madden, J. C., Enoch, S. J., Paini, A., & Cronin, M. (2020) · Alternatives to Laboratory Animals · doi:10.1177/0261192920965977 · Cited by 215 · see citing works
Self-citationReliable and explainable machine-learning methods for accelerated material discovery
Kailkhura, B., Gallagher, B. J., Kim, S., Hiszpanski, A. M., & Han, T. Y.-J. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0248-2 · Cited by 212 · see citing works
An Updated Review of Computer‐Aided Drug Design and Its Application to COVID‐19
Gurung, A. B., Ali, M. A., Lee, J., Farah, M. A., & Al‐Anazi, K. M. (2021) · BioMed Research International · doi:10.1155/2021/8853056 · Cited by 212 · see citing works
Machine learning models for drug–target interactions: current knowledge and future directions
D’Souza, S., Prema, K. V., & Balaji, S. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.03.003 · Cited by 208 · see citing works
Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists
Freeze, J. G., Kelly, H. R., & Batista, V. S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00759 · Cited by 207 · see citing works
A critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Self-citationSelvaraj, C., Chandra, I., & Singh, S. K. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10326-z · Cited by 206 · see citing works
Comprehensive Medicinal Chemistry III
(2017) · Elsevier eBooks · doi:10.1016/c2014-1-02188-6 · Cited by 205 · see citing works
Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond
Mannodi‐Kanakkithodi, A., Chandrasekaran, A. R., Kim, C., Huan, T. D., Pilania, G., Botu, V., & Ramprasad, R. (2017) · Materials Today · doi:10.1016/j.mattod.2017.11.021 · Cited by 201 · see citing works
A review on machine learning algorithms for the ionic liquid chemical space
Koutsoukos, S., Philippi, F., Malaret, F., & Welton, T. (2021) · Chemical Science · doi:10.1039/d1sc01000j · Cited by 200 · see citing works
Defining Molecular Initiating Events in the Adverse Outcome Pathway Framework for Risk Assessment
Allen, T. E. H., Goodman, J. M., Gutsell, S., & Russell, P. J. (2014) · Chemical Research in Toxicology · doi:10.1021/tx500345j · Cited by 199 · see citing works
Automation and computer-assisted planning for chemical synthesis
Shen, Y., Borowski, J. E., Hardy, M. A., Sarpong, R., Doyle, A. G., & Cernak, T. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00022-5 · Cited by 198 · see citing works
Wu, Z., Zhu, M., Kang, Y., Leung, E. L., Lei, T., Shen, C., Jiang, D., Wang, Z., Cao, D., & Hou, T. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa321 · Cited by 197 · see citing works
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
Chen, G., Shen, Z., Iyer, A., Ghumman, U. F., Tang, S., Bi, J., Chen, W., & Li, Y. (2020) · Polymers · doi:10.3390/polym12010163 · Cited by 193 · see citing works
Advancing Computational Toxicology by Interpretable Machine Learning
Jia, X., Wang, T., & Zhu, H. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00653 · Cited by 193 · see citing works
The History and Development of Quantitative Structure-Activity Relationships (QSARs)
Dearden, J. C. (2016) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.2016010101 · Cited by 192 · see citing works
Self-citationAn Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
Vo, A. H., Vleet, T. R. V., Gupta, R. R., Liguori, M. J., & Rao, M. S. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00227 · Cited by 191 · see citing works
Predicting Reaction Yields via Supervised Learning
Żurański, A. M., Alvarado, J. I. M., Shields, B. J., & Doyle, A. G. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00770 · Cited by 188 · see citing works
Conformational Effects on Physical-Organic Descriptors: The Case of Sterimol Steric Parameters
Brethomé, A. V., Fletcher, S. P., & Paton, R. S. (2019) · ACS Catalysis · doi:10.1021/acscatal.8b04043 · Cited by 187 · see citing works
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical PropertiesPreprint
Goh, G. B., Hodas, N. O., Siegel, C., & Vishnu, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.02034 · Cited by 186 · see citing works
Dixon, S. L., Duan, J., Smith, E., Bargen, C. D. V., Sherman, W., & Repasky, M. P. (2016) · Future Medicinal Chemistry · doi:10.4155/fmc-2016-0093 · Cited by 185 · see citing works
Ismaïli, L., Refouvelet, B., Benchekroun, M., Brogi, S., Brindisi, M., Gemma, S., Campiani, G., Filipić, S., Agbaba, D., Esteban, G., et al. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.12.003 · Cited by 184 · see citing works
A Safe‐by‐Design Strategy towards Safer Nanomaterials in Nanomedicines
Yan, L., Zhao, F., Wang, J., Zu, Y., Gu, Z., & Zhao, Y. (2019) · Advanced Materials · doi:10.1002/adma.201805391 · Cited by 184 · see citing works
Binding affinity in drug design: experimental and computational techniques
Kairys, V., Baranauskienė, L., Kazlauskiene, M., Matulis, D., & Kazlauskas, E. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1623202 · Cited by 184 · see citing works
Communicative Representation Learning on Attributed Molecular Graphs
Song, Y., Zheng, S., Niu, Z., Fu, Z.-H., Lu, Y., & Yang, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/392 · Cited by 183 · see citing works
Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives
Romano, J. D., & Tatonetti, N. P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00368 · Cited by 179 · see citing works
Piovesana, S., Capriotti, A. L., Cavaliere, C., Barbera, G. L., Montone, C. M., Chiozzi, R. Z., & Laganà, A. (2018) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-018-0852-x · Cited by 176 · see citing works
Li, P., Wang, J., Qiao, Y., Chen, H., Yu, Y., Yao, X., Gao, P., Xie, G., & Song, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab109 · Cited by 176 · see citing works
Mao, J., Akhtar, J., Zhang, X., Sun, L., Guan, S., Li, X., Chen, G., Liu, J., Jeon, H.-N., Kim, M. S., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103052 · Cited by 173 · see citing works
Chen, Y., Xiao, H., Zheng, J., & Liang, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121276 · Cited by 172 · see citing works
Guan, Y., Coley, C. W., Wu, H., Ranasinghe, D. S., Heid, E., Struble, T. J., Pattanaik, L., Green, W. H., & Jensen, K. F. (2020) · Chemical Science · doi:10.1039/d0sc04823b · Cited by 170 · see citing works
Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167 · see citing works
Bosc, N., Atkinson, F. L., Félix, E., Gaulton, A., Hersey, A., & Leach, A. R. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-018-0325-4 · Cited by 167 · see citing works
Tang, B., Kramer, S. T., Fang, M., Qiu, Y., Wu, Z., & Xu, D. H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0414-z · Cited by 167 · see citing works
Predicting Hepatotoxicity Using ToxCastin VitroBioactivity and Chemical Structure
Liu, J., Mansouri, K., Judson, R., Martin, M. T., Hong, H., Chen, M., Xu, X., Thomas, R. S., & Shah, I. (2015) · Chemical Research in Toxicology · doi:10.1021/tx500501h · Cited by 164 · see citing works
Small molecule compounds targeting miRNAs for cancer therapy
Monroig-Bosque, P. D. C., Chen, L., Zhang, S., & Călin, G. A. (2014) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2014.09.002 · Cited by 162 · see citing works
Aalizadeh, R., Alygizakis, ., Schymanski, E., Krauss, M. K. V., Schulze, T., Ibáñez, M., McEachran, A. D., Chao, A., Williams, A. J., Gago-Ferrero, P., et al. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c02348 · Cited by 162 · see citing works
Kolšek, K., Mavri, J., Dolenc, M. S., Gobec, S., & Turk, S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400649p · Cited by 160 · see citing works
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines
Li, M., Wang, Y., Zheng, R., Shi, X. M., Li, Y., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919581 · Cited by 160 · see citing works
Ballante, F., Kooistra, A. J., Kampen, S., de Graaf, C., & Carlsson, J. (2021) · Pharmacological Reviews · doi:10.1124/pharmrev.120.000246 · Cited by 160 · see citing works
Application of Computational Biology and Artificial Intelligence in Drug Design
Zhang, Y., Luo, M., Wu, P., Wu, S., Lee, T.-Y., & Bai, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113568 · Cited by 160 · see citing works
Yang, S., & Kar, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100011 · Cited by 160 · see citing works
DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs
Li, F., Hu, Q., Zhang, X., Sun, R., Liu, Z., Wu, S., Tian, S., Ma, X., Dai, Z., Yang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34807-3 · Cited by 159 · see citing works
Toward sustainable environmental quality: Priority research questions for Europe
den Brink, P. J. V., Boxall, A. B. A., Maltby, L., Brooks, B. W., Rudd, M. A., Backhaus, T., Spurgeon, D. J., Verougstraete, V., Ajao, C., Ankley, G. T., et al. (2018) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4205 · Cited by 159 · see citing works
An automated framework for QSAR model building
Kausar, S., & Falcão, A. O. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-017-0256-5 · Cited by 156 · see citing works
Zhang, Y., & Lee, A. A. (2019) · Chemical Science · doi:10.1039/c9sc00616h · Cited by 156 · see citing works
Application of in silico approaches for the generation of milk protein-derived bioactive peptides
Fitzgerald, R. J., Cermeño, M., Khalesi, M., Kleekayai, T., & Amigo‐Benavent, M. (2019) · Journal of Functional Foods · doi:10.1016/j.jff.2019.103636 · Cited by 155 · see citing works
Grambow, C. A., Pattanaik, L., & Green, W. H. (2020) · Scientific Data · doi:10.1038/s41597-020-0460-4 · Cited by 155 · see citing works
Cao, D., Xiao, N., Xu, Q., & Chen, A. F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu624 · Cited by 154 · see citing works
Cheng, W., & Ng, C. A. (2019) · Environmental Science & Technology · doi:10.1021/acs.est.9b04833 · Cited by 153 · see citing works
The Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151 · see citing works
- Authors
- Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., Consonni, V., Kuz’min, V. E., Cramer, R., Benigni, R., Yang, C., Rathman, J., Terfloth, L., Gasteiger, J., Richard, A., & Tropsha, A.
- Affiliations
- University of British Columbia (CA)
- University of North Carolina at Chapel Hill (US)
- Université de Strasbourg (FR)
- Lomonosov Moscow State University (RU)
- Liverpool John Moores University (GB)
- University of Insubria (IT)
- Martin Consulting, Waukegan, Illinois 60079, United States
- University of Milano-Bicocca (IT)
- Department of Molecular Structure and Cheminformatics, A. V. Bogatsky Physical-Chemical Institute, National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
- Tripos, Inc., St. Louis, Missouri 63144, United States
- Istituto Superiore di Sanità (IT)
- Altamira LLC, Columbus, Ohio 43235, United States
- The Ohio State University (US)
- Molecular Networks (Germany) (DE)
- Environmental Protection Agency (US)
- Corresponding author
- Tropsha, Alexander
- ORCID
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-01-06
- Volume
- 57
- Issue
- 12
- Pages
- 4977-5010
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- PMC
- PMC4074254
- OpenAlex
- W2033757486
- Cited by
- 2,164
- References
- 288
- Citations by year
- 2026: 288 · 2025: 225 · 2024: 181 · 2023: 173 · 2022: 211 · 2021: 231 · 2020: 220 · 2019: 167 · 2018: 154 · 2017: 137 · 2016: 81 · 2015: 63 · 2014: 30 · 2012: 1
- FWCI
- 63.86
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, public-domain)
- HAL (submittedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- IrInSubria (University of Insubria) (submittedVersion)
- UNC Libraries
- Funders
- U.S. Environmental Protection Agency (RD 83499901, 83499901)
- National Institutes of Health (5r01gm096967-03, GM 096967)
- Natural Sciences and Engineering Research Council of Canada
- National Institute of General Medical Sciences (GM096967, GM066940, R01GM066940, R01-GM096967)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- QSAR, QSAR model validation
- MeSH terms
- Artificial Intelligence, Models, Molecular, Pharmacokinetics, Quantum Theory, Toxicology, Drug Design, Databases, Factual, Quantitative Structure-Activity Relationship, Antimicrobial Cationic Peptides, Complex Mixtures, Nanostructures, History, 20th Century, History, 21st Century
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