{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:01:04Z","timestamp":1787493664508,"version":"build-2736575974"},"reference-count":119,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology","doi-asserted-by":"publisher","award":["SR\/S2\/JCB-09\/2009"],"award-info":[{"award-number":["SR\/S2\/JCB-09\/2009"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>The preclinical drug discovery stage often requires a large amount of costly and time-consuming experiments using huge sets of chemical compounds. In the last few decades, this process has undergone significant improvements by the introduction of quantitative structure-activity relationship (QSAR) modelling that uses a certain percentage of experimental data to predict the biological activity\/property of compounds with similar structural skeleton and\/or containing a particular functional group(s). The use of machine learning tools along with it has made life even easier for pharmaceutical researchers. Here, we discuss the toxicity of certain sets of bioactive compounds towards Pimephales promelas and Tetrahymena pyriformis in terms of the global conceptual density functional theory (CDFT)-based descriptor, electrophilicity index (\u03c9). We have compared the results with those obtained by using the commonly used hydrophobicity parameter, logP (where P is the n-octanol\/water partition coefficient), considering the greater ease of computing the \u03c9 descriptor. The Human African trypanosomiasis (HAT) curing activity of 32 pyridyl benzamide derivatives is also studied against Tryphanosoma brucei. In this review article, we summarize these multiple linear regression (MLR)-based QSAR studies in terms of electrophilicity (\u03c9, \u03c92) and hydrophobicity (logP, (logP)2) parameters.<\/jats:p>","DOI":"10.3390\/ph15111383","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T19:16:23Z","timestamp":1668107783000},"page":"1383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Quantitative Structure\u2013Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5231-4819","authenticated-orcid":false,"given":"Ranita","family":"Pal","sequence":"first","affiliation":[{"name":"Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur 721302, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3084-1495","authenticated-orcid":false,"given":"Shanti Gopal","family":"Patra","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5650-7666","authenticated-orcid":false,"given":"Pratim Kumar","family":"Chattaraj","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","unstructured":"Karcher, W., and Devillers, J. (1990). SAR and QSAR in environmental chemistry and toxicology: Scientific tool or wishful thinking?. Practical Applications of Quantitative Structure\u2013Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.2174\/1568026023392823","article-title":"QSAR: Then and Now","volume":"2","author":"Selassie","year":"2002","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1517\/17460440903307409","article-title":"Advances in quantitative structure\u2013activity relationship models of antioxidants","volume":"4","author":"Roy","year":"2009","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0166-1280(02)00614-0","article-title":"Quantitative structure\u2013activity relationships (QSARs) in toxicology: A historical perspective","volume":"622","author":"Schultz","year":"2003","journal-title":"J. Mol. Struct. THEOCHEM"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0166-1280(02)00615-2","article-title":"The present status of QSAR in toxicology","volume":"622","author":"Schultz","year":"2003","journal-title":"J. Mol. Struct. THEOCHEM"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gombar, V.K., Mattioni, B.E., Zwickl, C., and Deahl, J.T. (2006). Computational Approaches for Assessment of Toxicity: A Historical Perspective and Current Status. Computational Toxicology, John Wiley & Sons, Inc.","DOI":"10.1002\/9780470145890.ch7"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/194178b0","article-title":"Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients","volume":"194","author":"Hansch","year":"1962","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1021\/cr0102009","article-title":"Chem-bioinformatics: Comparative QSAR at the interface between chemistry and biology","volume":"102","author":"Hansch","year":"2002","journal-title":"Chem. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Katritzky, A.R., Kuanar, M., Slavov, S., Hall, C.D., Karelson, M., Kahn, I., and Dobchev, D.A. (2011). ChemInform Abstract: Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction. ChemInform, 42.","DOI":"10.1002\/chin.201120272"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1021\/acs.jcim.5b00229","article-title":"Understanding the Roles of the \u201ctwo QSARs\u201d","volume":"56","author":"Fujita","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104384","DOI":"10.1016\/j.chemolab.2021.104384","article-title":"Quantum chemical descriptors in quantitative structure\u2013activity relationship models and their applications","volume":"217","author":"Wang","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1021\/ci900224u","article-title":"Local indices for similarity analysis (LISA)-A 3D-QSAR formalism based on local molecular similarity","volume":"49","author":"Verina","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1021\/cr100125d","article-title":"Use of 13C NMR chemical shift as QSAR\/QSPR descriptor","volume":"111","author":"Verma","year":"2011","journal-title":"Chem. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1021\/jm00300a040","article-title":"On the parabolic relationship between drug potency and hydrophobicity","volume":"13","author":"McFarland","year":"1970","journal-title":"J. Med. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1021\/ci049628+","article-title":"K nearest neighbors QSAR modeling as a variational problem: Theory and applications","volume":"45","author":"Itskowitz","year":"2005","journal-title":"J. Chem. Inf. Model."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1021\/ac403544k","article-title":"Quantitative structure-property relationship modeling: A valuable support in high-throughput screening quality control","volume":"86","author":"Ruggiu","year":"2014","journal-title":"Anal. Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13093","DOI":"10.1021\/acs.chemrev.5b00215","article-title":"A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes","volume":"115","author":"Fayet","year":"2015","journal-title":"Chem. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4384","DOI":"10.1007\/s00044-012-0430-2","article-title":"QSAR analysis of some 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 inhibitors using genetic algorithm-least square support vector machine","volume":"22","author":"Shahlaei","year":"2013","journal-title":"Med. Chem. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci9903206","article-title":"Structurally Diverse Quantitative Structure-Property Relationship Correlations of Technologically Relevant Physical Properties","volume":"40","author":"Katritzky","year":"2000","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1007\/s00044-010-9501-4","article-title":"Comparative quantitative structure-activity relationship study of some 1-aminocyclopentyl-3-carboxyamides as CCR2 inhibitors using stepwise MLR, FA-MLR, and GA-PLS","volume":"21","author":"Shahlaei","year":"2012","journal-title":"Med. Chem. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1017\/S0031182012000169","article-title":"Update on field use of the available drugs for the chemotherapy of human African trypanosomiasis","volume":"139","author":"Simarro","year":"2012","journal-title":"Parasitology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00214-020-02652-6","article-title":"Revisiting immiscibility through DFT chemical descriptors","volume":"139","author":"Vigneresse","year":"2020","journal-title":"Theor. Chem. Acc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1021\/cr200097p","article-title":"Quantitative Structure\u2013Activity Relationship Studies on Na+, K+-ATPase Inhibitors","volume":"112","author":"Gupta","year":"2012","journal-title":"Chem. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6776","DOI":"10.1021\/jf401092h","article-title":"Molecular docking and QSAR studies: Noncovalent interaction between acephate analogous and the receptor site of human acetylcholinesterase","volume":"61","author":"Gholivand","year":"2013","journal-title":"J. Agric. Food Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1021\/ct301129x","article-title":"Physicochemical properties of hazardous energetic compounds from molecular simulation","volume":"9","author":"Ahmed","year":"2013","journal-title":"J. Chem. Theory Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6427","DOI":"10.1021\/cr500483r","article-title":"QSAR Studies on Hydroxamic Acids: A Fascinating Family of Chemicals with a Wide Spectrum of Activities","volume":"115","author":"Gupta","year":"2015","journal-title":"Chem. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.3390\/ijms11031020","article-title":"A review on progress in QSPR studies for surfactants","volume":"11","author":"Hu","year":"2010","journal-title":"Int. J. Mol. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.tifs.2021.05.031","article-title":"Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects","volume":"114","author":"Bo","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104956","DOI":"10.1016\/j.yrtph.2021.104956","article-title":"Determination of \u201cfitness-for-purpose\u201d of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use","volume":"123","author":"Belfield","year":"2021","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1021\/jm4004285","article-title":"QSAR modeling: Where have you been? Where are you going to?","volume":"57","author":"Cherkasov","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1021\/ci600332j","article-title":"Contemporary QSAR Classifiers Compared","volume":"47","author":"Bruce","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6393","DOI":"10.1021\/jm500191u","article-title":"Pyridyl benzamides as a novel class of potent inhibitors for the kinetoplastid Trypanosoma brucei","volume":"57","author":"Ferrins","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1021\/ac970464o","article-title":"Quantitative Retention-Structure and Retention-Activity Relationship Studies of Local Anesthetics by Micellar Liquid Chromatography","volume":"70","author":"Sagrado","year":"1998","journal-title":"Anal. Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1021\/ie2014668","article-title":"Systematic Multiscale Method for Studying the Structure\u2013Performance Relationship of Drug-Delivery Systems","volume":"51","author":"Guo","year":"2012","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1021\/ci000459c","article-title":"Quantitative Structure-Activity Relationship Studies Using Gaussian Processes","volume":"41","author":"Burden","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/S0968-0896(02)00055-X","article-title":"Study on quantitative structure\u2013toxicity relationships of benzene derivatives acting by narcosis","volume":"10","author":"Khadikar","year":"2002","journal-title":"Bioorg. Med. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1021\/tx050322m","article-title":"Group Philicity and Electrophilicity as Possible Descriptors for Modeling Ecotoxicity Applied to Chlorophenols","volume":"19","author":"Padmanabhan","year":"2006","journal-title":"Chem. Res. Toxicol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10346","DOI":"10.1021\/jp035620b","article-title":"Chemical Reactivity Profiles of Two Selected Polychlorinated Biphenyls","volume":"107","author":"Parthasarathi","year":"2003","journal-title":"J. Phys. Chem. A"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5533","DOI":"10.1016\/j.bmc.2004.08.013","article-title":"Electrophilicity index as a possible descriptor of biological activity","volume":"12","author":"Parthasarathi","year":"2004","journal-title":"Bioorg. Med. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1002\/etc.5620160514","article-title":"Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales Promelas)","volume":"16","author":"Russom","year":"1997","journal-title":"Environ. Toxicol. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/(SICI)1521-3838(199804)17:02<131::AID-QSAR131>3.0.CO;2-L","article-title":"Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis\u2014Theoretical Considerations","volume":"17","author":"Zhao","year":"1998","journal-title":"Quant. Struct. Relatsh."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1021\/cr950202r","article-title":"Quantum-Chemical Descriptors in QSAR\/QSPR Studies","volume":"96","author":"Karelson","year":"1996","journal-title":"Chem. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"14865","DOI":"10.1039\/c1cp21430f","article-title":"Aromaticity in all-metal annular systems: The counter-ion effect","volume":"13","author":"Chakraborty","year":"2011","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"PR46","DOI":"10.1021\/cr078014b","article-title":"Update 1 of: Electrophilicity Index","volume":"107","author":"Chattaraj","year":"2007","journal-title":"Chem. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1021\/cr040109f","article-title":"Electrophilicity Index","volume":"106","author":"Chattaraj","year":"2006","journal-title":"Chem. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1021\/cr990029p","article-title":"Conceptual Density Functional Theory","volume":"103","author":"Geerlings","year":"2003","journal-title":"Chem. Rev."},{"key":"ref_48","unstructured":"Parr, R.G., and Yang, W. (1989). Density-Functional Theory of Atoms and Molecules, Oxford University Press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s00214-020-2546-7","article-title":"Conceptual density functional theory: Status, prospects, issues","volume":"139","author":"Geerlings","year":"2020","journal-title":"Theor. Chem. Acc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6540","DOI":"10.1021\/jp055849m","article-title":"p K a Prediction Using Group Philicity","volume":"110","author":"Parthasarathi","year":"2006","journal-title":"J. Phys. Chem. A"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1021\/ja983494x","article-title":"Electrophilicity Index","volume":"121","author":"Parr","year":"1999","journal-title":"J. Am. Chem. Soc."},{"key":"ref_52","first-page":"798","article-title":"Toxicity analysis of benzidine through chemical reactivity and selectivity profiles: A DFT approach","volume":"2","author":"Parthasarathi","year":"2003","journal-title":"Internet Electron. J. Mol. Des."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1504\/IJAPR.2016.076987","article-title":"Performance enhancement of the pattern recalling efficiency of Hopfield neural network using genetic algorithm for cursive handwritten character recognition","volume":"3","author":"Bansal","year":"2016","journal-title":"Int. J. Appl. Pattern Recognit."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jbi.2017.05.002","article-title":"Character-level neural network for biomedical named entity recognition","volume":"70","author":"Gridach","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hertz, J., Krogh, A., and Palmer, R.G. (2018). Introduction to the Theory of Neural Computation, CRC Press.","DOI":"10.1201\/9780429499661"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1111\/cbdd.13428","article-title":"Hydrophobicity versus electrophilicity: A new protocol toward quantitative structure\u2013toxicity relationship","volume":"93","author":"Pal","year":"2019","journal-title":"Chem. Biol. Drug Des."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bianucci, A.M., Micheli, A., Sperduti, A., and Starita, A. (2003). A Novel Approach to QSPR\/QSAR Based on Neural Networks for Structures. Soft Computing Approaches in Chemistry, Springer.","DOI":"10.1007\/978-3-540-36213-5_10"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1021\/ja01062a035","article-title":"p-\u03c3-\u03c0 Analysis. A Method for the Correlation of Biological Activity and Chemical Structure","volume":"86","author":"Hansch","year":"1964","journal-title":"J. Am. Chem. Soc."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kubinyi, H. (1993). Methods and Principles in Medicinal Chemistry. QSAR: Hansch Analysis and Related Approaches, Wiley-VCH.","DOI":"10.1002\/9783527616824"},{"key":"ref_60","unstructured":"Pauling, L. (1960). The Nature of the Chemical Bond, Cornell University Press. [3rd ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sen, K., and Jorgenson, C. (1987). Electronegativity. Structure and Bonding Bonding, Springer.","DOI":"10.1007\/BFb0029833"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Pearson, R.G. (1997). Chemical Hardness, WileyVCH.","DOI":"10.1002\/3527606173"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chattaraj, P.K. (2009). Chemical Reactivity Theory, CRC Press.","DOI":"10.1201\/9781420065442"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1063\/1.436185","article-title":"Electronegativity: The density functional viewpoint","volume":"68","author":"Parr","year":"1977","journal-title":"J. Chem. Phys."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3570","DOI":"10.1021\/ja01348a011","article-title":"The nature of the chemical bond. IV. the energy of single bonds and the relative electronegativity of atoms","volume":"54","author":"Pauling","year":"1932","journal-title":"J. Am. Chem. Soc."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1063\/1.1749394","article-title":"A New Electroaffinity Scale; Together with Data on Valence States and on Valence Ionization Potentials and Electron Affinities","volume":"2","author":"Mulliken","year":"1934","journal-title":"J. Chem. Phys."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/0022-1902(58)80003-2","article-title":"A scale of electronegativity based on electrostatic force","volume":"5","author":"Allred","year":"1958","journal-title":"J. Inorg. Nucl. Chem."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"B864","DOI":"10.1103\/PhysRev.136.B864","article-title":"Inhomogeneous Electron Gas","volume":"136","author":"Hohenberg","year":"1964","journal-title":"Phys. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"A1133","DOI":"10.1103\/PhysRev.140.A1133","article-title":"Self-Consistent Equations Including Exchange and Correlation Effects","volume":"140","author":"Kohn","year":"1965","journal-title":"Phys. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ingold, C.K. (1933). 266. Significance of tautomerism and of the reactions of aromatic compounds in the electronic theory of organic reactions. J. Chem. Soc., 1120\u20131127.","DOI":"10.1039\/jr9330001120"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1021\/cr60051a003","article-title":"Principles of an Electronic Theory of Organic Reactions","volume":"15","author":"Ingold","year":"1934","journal-title":"Chem. Rev."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"11578","DOI":"10.1073\/pnas.95.20.11578","article-title":"Reactivity of the HIV-1 nucleocapsid protein p7 zinc finger domains from the perspective of density-functional theory","volume":"95","author":"Maynard","year":"1998","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_73","first-page":"10101","article-title":"Book Review","volume":"47","author":"Atoms","year":"1989","journal-title":"Density Funct. Theory Atoms Mol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/S0031-8914(34)90011-2","article-title":"\u00dcber die Zuordnung von Wellenfunktionen und Eigenwerten zu den Einzelnen Elektronen Eines Atoms","volume":"1","author":"Koopmans","year":"1934","journal-title":"Physica"},{"key":"ref_75","first-page":"358","article-title":"Nonlinear correlations between \u03bd 1 Raman band and global scalar properties for different length carotenoids","volume":"58","year":"2019","journal-title":"Lith. J. Phys."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Flores-Holgu\u00edn, N., Frau, J., and Glossman-Mitnik, D. (2020). Conceptual DFT-Based Computational Peptidology of Marine Natural Compounds: Discodermins A\u2013H. Molecules, 25.","DOI":"10.20944\/preprints202008.0351.v1"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s11030-005-9009-x","article-title":"Analyzing toxicity through electrophilicity","volume":"10","author":"Roy","year":"2006","journal-title":"Mol. Divers."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1016\/j.bmc.2005.03.011","article-title":"Electrophilicity as a possible descriptor for toxicity prediction","volume":"13","author":"Roy","year":"2005","journal-title":"Bioorg. Med. Chem."},{"key":"ref_79","first-page":"57","article-title":"An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei","volume":"8","author":"Pal","year":"2019","journal-title":"Int. J. Chemoinform. Chem. Eng."},{"key":"ref_80","unstructured":"Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Scalmani, G., Barone, V., Petersson, G.A., and Nakatsuji, H. (2016). Gaussian 16, Gaussian Inc."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Duchowicz, P. (2018). Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors. Cells, 7.","DOI":"10.3390\/cells7020013"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Peter, S.C., Dhanjal, J.K., Malik, V., Radhakrishnan, N., Jayakanthan, M., and Sundar, D. (2019). Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications. Encyclopedia of Bioinformatics and Computational Biology, Elsevier.","DOI":"10.1016\/B978-0-12-809633-8.20197-0"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.3390\/ijms10051978","article-title":"Current mathematical methods used in QSAR\/QSPR studies","volume":"10","author":"Liu","year":"2009","journal-title":"Int. J. Mol. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1021\/ci960487o","article-title":"QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives","volume":"37","author":"Luco","year":"1997","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Liman, W., Oubahmane, M., Hdoufane, I., Bjij, I., Villemin, D., Daoud, R., Cherqaoui, D., and El Allali, A. (2022). Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus. Molecules, 27.","DOI":"10.3390\/molecules27092729"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1021\/ci010245a","article-title":"Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides, and COX-2 inhibitors","volume":"42","author":"Liu","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4070","DOI":"10.1021\/acs.jcim.9b00476","article-title":"New workflow for QSAR model development from small data sets: Small dataset curator and small dataset modeler. Integration of data curation, exhaustive double cross-validation, and a set of optimal model selection techniques","volume":"59","author":"Ambure","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"16771","DOI":"10.1038\/s41598-020-73681-1","article-title":"Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery","volume":"10","author":"Tsou","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"103052","DOI":"10.1016\/j.isci.2021.103052","article-title":"Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models","volume":"24","author":"Mao","year":"2021","journal-title":"iScience"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/frai.2019.00017","article-title":"Descriptor Free QSAR Modeling Using Deep Learning with Long Short-Term Memory Neural Networks","volume":"2","author":"Chakravarti","year":"2019","journal-title":"Front. Artif. Intell."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Stitou, M., Toufik, H., Bouachrine, M., Bih, H., and Lamchouri, F. (2019, January 26\u201327). Machine learning algorithms used in Quantitative structure\u2014Activity relationships studies as new approaches in drug discovery. Proceedings of the 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), Taza, Morocco.","DOI":"10.1109\/ISACS48493.2019.9068917"},{"key":"ref_92","first-page":"4188","article-title":"Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells","volume":"17","author":"Ancuceanu","year":"2019","journal-title":"Oncol. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"17","DOI":"10.2174\/1570163817666200316104404","article-title":"An Analysis of QSAR Research Based on Machine Learning Concepts","volume":"18","author":"Keyvanpour","year":"2021","journal-title":"Curr. Drug Discov. Technol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1021\/ci020039i","article-title":"QSAR\/QSPR studies using probabilistic neural networks and generalized regression neural networks","volume":"42","author":"Mosier","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1021\/ci200128w","article-title":"Classifying molecules using a sparse probabilistic kernel binary classifier","volume":"51","author":"Lowe","year":"2011","journal-title":"J. Chem. Inf. Model."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1002\/qsar.200810101","article-title":"Artificial neural network-based quantitative structural property relationship for predicting boiling points of refrigerants","volume":"28","author":"Ghasemi","year":"2009","journal-title":"QSAR Comb. Sci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.ejmech.2009.12.066","article-title":"Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: A comparative study","volume":"45","author":"Shahlaei","year":"2010","journal-title":"Eur. J. Med. Chem."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1021\/ci0501286","article-title":"Three-dimensional QSAR using the k-nearest neighbor method and its interpretation","volume":"46","author":"Ajmani","year":"2006","journal-title":"J. Chem. Inf. Model."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.1021\/mp300237z","article-title":"Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions","volume":"9","author":"Myint","year":"2012","journal-title":"Mol. Pharm."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1021\/ci000462w","article-title":"Neural network based temperature-dependent quantitative structure property relations (QSPRs) for predicting vapor pressure of hydrocarbons","volume":"41","author":"Yaffe","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1021\/ci000450a","article-title":"Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks","volume":"40","author":"Burden","year":"2000","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1021\/ie0008068","article-title":"A fuzzy ARTMAP-based Quantitative Structure-Property Relationship (QSPR) for predicting physical properties of organic compounds","volume":"40","author":"Espinosa","year":"2001","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3183","DOI":"10.1021\/jm980697n","article-title":"Robust QSAR models using bayesian regularized neural networks","volume":"42","author":"Burden","year":"1999","journal-title":"J. Med. Chem."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1021\/ci500255v","article-title":"Erratum: \u201cin silico target predictions: Defining a benchmarking data set and comparison of performance of the multiclass na\u00efve bayes and parzen-rosenblatt window\u201d","volume":"54","author":"Koutsoukas","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.asoc.2017.09.040","article-title":"Deep neural network in QSAR studies using deep belief network","volume":"62","author":"Ghasemi","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/1062936032000101501","article-title":"Partial Least Squares modelling of the acute toxicity of aliphatic compounds to Tetrahymena pyriformis","volume":"14","author":"Netzeva","year":"2003","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1021\/tx025589p","article-title":"Structure-toxicity relationships for aliphatic chemicals evaluated with Tetrahymena pyriformis","volume":"15","author":"Schultz","year":"2002","journal-title":"Chem. Res. Toxicol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.theochem.2004.04.020","article-title":"QSAR modelling of aldehyde toxicity against a protozoan, Tetrahymena pyriformis by optimization of correlation weights of nearest neighboring codes","volume":"679","author":"Toropov","year":"2004","journal-title":"J. Mol. Struct. THEOCHEM"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.theochem.2003.08.056","article-title":"QSAR study of the toxic action of aliphatic compounds to the bacteria Vibrio fisheri based on correlation weighting of local graph invariants","volume":"639","author":"Castro","year":"2003","journal-title":"J. Mol. Struct. THEOCHEM"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s12039-007-0061-1","article-title":"An atom counting and electrophilicity based QSTR approach","volume":"119","author":"Chattaraj","year":"2007","journal-title":"J. Chem. Sci."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1023\/A:1019166802430","article-title":"Simple analytical method for evaluation of statistical importance of correlations in QSAR studies","volume":"27","author":"Pecka","year":"2000","journal-title":"J. Math. Chem."},{"key":"ref_112","first-page":"535","article-title":"Toxicity analysis of 33\u203244\u20325-pentachloro biphenyl through chemical reactivity and selectivity profiles","volume":"86","author":"Parthasarathi","year":"2004","journal-title":"Curr. Sci."},{"key":"ref_113","unstructured":"Russell, R.C.G., Williams, N.S., and Bulstrode, C.J.K. (2000). Bailey & Love\u2019s Short Practice of Surgery, Arnold. [23rd ed.]."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"e26097","DOI":"10.1002\/qua.26097","article-title":"Quantitative structure-toxicity relationship: An \u201cin silico study\u201d using electrophilicity and hydrophobicity as descriptors","volume":"120","author":"Jana","year":"2020","journal-title":"Int. J. Quantum Chem."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1007\/s00044-016-1664-1","article-title":"Quantitative structure\u2013activity relationships (QSARs) and pharmacophore modeling for human African trypanosomiasis (HAT) activity of pyridyl benzamides and 3-(oxazolo [4,5-b]pyridin-2-yl)anilides","volume":"25","author":"Masand","year":"2016","journal-title":"Med. Chem. Res."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.trac.2009.09.009","article-title":"Sum of ranking differences compares methods or models fairly","volume":"29","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.chemolab.2013.06.007","article-title":"Method and model comparison by sum of ranking differences in cases of repeated observations (ties)","volume":"127","year":"2013","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s11030-009-9133-0","article-title":"Arsenic toxicity: An atom counting and electrophilicity-based protocol","volume":"13","author":"Roy","year":"2009","journal-title":"Mol. Divers."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0009-2614(00)00488-7","article-title":"Application of quantum chemical descriptor in quantitative structure activity and structure property relationship","volume":"323","author":"Thanikaivelan","year":"2000","journal-title":"Chem. Phys. Lett."}],"container-title":["Pharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8247\/15\/11\/1383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:54Z","timestamp":1760145234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8247\/15\/11\/1383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":119,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["ph15111383"],"URL":"https:\/\/doi.org\/10.3390\/ph15111383","relation":{},"ISSN":["1424-8247"],"issn-type":[{"value":"1424-8247","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,10]]}}}