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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Review Article

Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use

Author(s): Marjana Novič*

Volume 23, Issue 29, 2023

Published on: 20 October, 2023

Page: [2792 - 2804] Pages: 13

DOI: 10.2174/0115680266251327231017053718

Price: $65

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Abstract

Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundation for data-driven modelling approaches that have fostered the development of machine learning and artificial intelligence tools. An overview and discussion of the principles of QSAR modelling focus on the assembly and curation of data, computation of molecular descriptor, optimization, validation, and definition of the scope of the developed QSAR models. In this review, some examples of (QSAR) models based on artificial neural networks are given to demonstrate the effectiveness of nonlinear methods for extracting information from large data sets to classify new chemicals and predict their biological properties.

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[1]
Muir, R.M.; Hansch, C. Azulene derivatives as plant growth regulators. Nature, 1961, 190(4777), 741-742.
[http://dx.doi.org/10.1038/190741a0] [PMID: 13773632]
[2]
Hansch, C.; Maloney, P.P.; Fujita, T.; Muir, R.M. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, 1962, 194, 178-180.
[3]
Hansch, C.; Fujita, T. p -σ-π Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc., 1964, 86(8), 1616-1626.
[http://dx.doi.org/10.1021/ja01062a035]
[4]
Muir, R.M.; Hansch, C. Chem-bioinformatics and QSAR: A review of qsar lacking positive hydrophobic terms. Nature, 1969, 190, 741-742.
[http://dx.doi.org/10.1038/190741a0] [PMID: 13773632]
[5]
Schaeffer, H.J.; Johnson, R.N.; Odin, E.; Hansch, C. Structure-activity relations in adenosine deaminase inhibitors. J. Med. Chem., 1970, 13(3), 452-455.
[http://dx.doi.org/10.1021/jm00297a026] [PMID: 5441130]
[6]
Craig, P.N.; Hansch, C.H.; McFarland, J.W.; Martin, Y.C.; Purcell, W.P.; Zahradník, R. Minimal statistical data for structure-function correlations. J. Med. Chem., 1971, 14(5), 447.
[http://dx.doi.org/10.1021/jm00287a018] [PMID: 5117692]
[7]
Hansch, C.; Helmkamp, G. Organic Chemistry, 2nd; McGraw-Hill Book Co.: New York, 1963.
[8]
Hansch, C.; Kurup, A.; Garg, R.; Gao, H. Chem-bioinformatics and QSAR: A review of QSAR lacking positive hydrophobic terms. Chem. Rev., 2001, 101(3), 619-672.
[http://dx.doi.org/10.1021/cr0000067] [PMID: 11712499]
[9]
Karelson, M. Molecualr descriptors in QSAR/QSPR; Wiley: New York, Chichester, 2000.
[10]
Katritzky, A.R.; Pacureanu, L.M.; Dobchev, D.A.; Fara, D.C.; Duchowicz, P.R.; Karelson, M. QSAR modeling of the inhibition of glycogen synthase kinase-3. Bioorg. Med. Chem., 2006, 14(14), 4987-5002.
[http://dx.doi.org/10.1016/j.bmc.2006.03.009] [PMID: 16650999]
[11]
Kode, D.R.A.G.O.N. (Software for Molecular Descriptor Calculation) Version 7.0; Kode, Pisa, Ed.; Italy, 2016.
[12]
Mauri, A. A tool to calculate and analyze molecular descriptors and fingerprints.Ecotoxicological QSARs; Roy, K., Ed.; Springer nature, 2020, pp. 801-820.
[13]
Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]
[14]
OECD; Guideline no. ENV/JM/MONO 2.Guidance on the Principle of Measure of Goodness-of-Fit; OECD. Robustness and Predictivity: Paris, France, 2007, p. 103.

[15]
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci., 2007, 26(5), 694-701.
[http://dx.doi.org/10.1002/qsar.200610151]
[16]
Gramatica, P. External evaluation of qsar models, in addition to cross-validation: Verification of predictive capability on totally new chemicals. Mol. Inform., 2014, 33(4), 311-314.
[http://dx.doi.org/10.1002/minf.201400030] [PMID: 27485777]
[17]
Chirico, N.; Gramatica, P. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J. Chem. Inf. Model., 2012, 52(8), 2044-2058.
[http://dx.doi.org/10.1021/ci300084j] [PMID: 22721530]
[18]
Todeschini, R.; Consonni, V.; Gramatica, P. Chemometrics in QSAR.Comprehensive Chemometrics; Brown, S.; Tauler, R.; Walczak, B., Eds.; Elsevier: Oxford, UK, 2009, pp. 129-172.
[http://dx.doi.org/10.1016/B978-044452701-1.00007-7]
[19]
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. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010.
[http://dx.doi.org/10.1021/jm4004285] [PMID: 24351051]
[20]
Emmert-Streib, F.; Yli-Harja, O.; Dehmer, M. Artificial intelligence: A clarification of misconceptions, myths and desired status. Front. Artif. Intell., 2020, 3, 524339.
[http://dx.doi.org/10.3389/frai.2020.524339] [PMID: 33733197]
[21]
Emmert-Streib, F.; Yang, Z.; Feng, H.; Tripathi, S.; Dehmer, M. An introductory review of deep learning for prediction models with big data. Front. Artif. Intell., 2020, 3, 4.
[http://dx.doi.org/10.3389/frai.2020.00004] [PMID: 33733124]
[22]
Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data, 2021, 8(1), 53.
[http://dx.doi.org/10.1186/s40537-021-00444-8] [PMID: 33816053]
[23]
Jiménez-Luna, J.; Cuzzolin, A.; Bolcato, G.; Sturlese, M.; Moro, S. A deep-learning approach toward rational molecular docking protocol selection. Molecules, 2020, 25(11), 2487.
[http://dx.doi.org/10.3390/molecules25112487] [PMID: 32471211]
[24]
Dayhoff, J. Neural network architectures, An introduction; Van Nostrand Reinhold: New York, 1990.
[25]
Hecht-Nielsen, R. Counterpropagation networks. Appl. Opt., 1987, 26(23), 4979-4983.
[http://dx.doi.org/10.1364/AO.26.004979] [PMID: 20523476]
[26]
Zupan, J.; Novič, M.; Ruisánchez, I. Kohonen and counterpropagation artificial neural networks in analytical chemistry. Chemom. Intell. Lab. Syst., 1997, 38(1), 1-23.
[http://dx.doi.org/10.1016/S0169-7439(97)00030-0]
[27]
Kuzmanovski, I.; Novič, M. Counter-propagation neural networks in Matlab. Chemom. Intell. Lab. Syst., 2008, 90(1), 84-91.
[http://dx.doi.org/10.1016/j.chemolab.2007.07.003]
[28]
Stojković, G.; Novič, M.; Kuzmanovski, I. Counter-propagation artificial neural networks as a tool for prediction of pKBH+ for series of amides. Chemom. Intell. Lab. Syst., 2010, 102(2), 123-129.
[http://dx.doi.org/10.1016/j.chemolab.2010.04.013]
[29]
Drgan, V.; Župerl, Š.; Vračko, M.; Cappelli, C.I.; Novič, M. CPANNatNIC software for counter-propagation neural network to assist in read-across. J. Cheminform., 2017, 9.
[30]
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[http://dx.doi.org/10.1016/S1093-3263(01)00123-1] [PMID: 11858635]
[31]
Alexander Tropsha, P.G.; Vijay, K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR Models. QSAR Comb. Sci., 2003, 22, 9.
[32]
Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 2010, 50(7), 1189-1204.
[http://dx.doi.org/10.1021/ci100176x] [PMID: 20572635]
[33]
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[http://dx.doi.org/10.1002/minf.201000061] [PMID: 27463326]
[34]
Gramatica, P. Principles of QSAR modeling. Int. J. Quant. Struc.Prop. Relat., 2020, 5(3), 61-97.
[http://dx.doi.org/10.4018/IJQSPR.20200701.oa1]
[35]
Roy, K.; Mandal, A.S. Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. J. Enzyme Inhib. Med. Chem., 2008, 23(6), 980-995.
[http://dx.doi.org/10.1080/14756360701811379] [PMID: 18608761]
[36]
Baskin, I.I.; Palyulin, V.A.; Zefirov, N.S. Neural networks in building QSAR models. Methods Mol. Biol., 2008, 458, 137-158.
[PMID: 19065809]
[37]
Chakravarti, S.K.; Alla, S.R.M. Descriptor free QSAR modeling using deep learning with long short-term memory neural networks. Front. Artif. Intell., 2019, 2, 17.
[http://dx.doi.org/10.3389/frai.2019.00017] [PMID: 33733106]
[38]
Ghasemi, F.; Mehridehnavi, A.; Pérez-Garrido, A.; Pérez-Sánchez, H. Neural network and deep-learning algorithms used in QSAR studies: Merits and drawbacks. Drug Discov. Today, 2018, 23(10), 1784-1790.
[http://dx.doi.org/10.1016/j.drudis.2018.06.016] [PMID: 29936244]
[39]
Xu, Y. Deep neural networks for QSAR. Methods Mol. Biol., 2022, 2390, 233-260.
[http://dx.doi.org/10.1007/978-1-0716-1787-8_10] [PMID: 34731472]
[40]
Carracedo-Reboredo, P.; Liñares-Blanco, J.; Rodríguez-Fernández, N.; Cedrón, F.; Novoa, F.J.; Carballal, A.; Maojo, V.; Pazos, A.; Fernandez-Lozano, C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J., 2021, 19, 4538-4558.
[http://dx.doi.org/10.1016/j.csbj.2021.08.011] [PMID: 34471498]
[41]
Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature, 1986, 323(6088), 533-536.
[http://dx.doi.org/10.1038/323533a0]
[42]
Zupan, J.; Gasteiger, J. Neural Networks in Chemistry and Drug Design; Wiley: Weinheim, 1999.
[43]
Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern., 1982, 43(1), 59-69.
[http://dx.doi.org/10.1007/BF00337288]
[44]
Kohonen, T. Self-Organizing Maps, 3rd; Springer: Berlin, 2001.
[http://dx.doi.org/10.1007/978-3-642-56927-2]
[45]
Mora Lagares, L.; Minovski, N.; Caballero Alfonso, A.Y.; Benfenati, E.; Wellens, S.; Culot, M.; Gosselet, F.; Novič, M. Homology modeling of the human p-glycoprotein (ABCB1) and insights into ligand binding through molecular docking studies. Int. J. Mol. Sci., 2020, 21(11), 4058.
[http://dx.doi.org/10.3390/ijms21114058] [PMID: 32517082]
[46]
Mora Lagares, L.; Minovski, N.; Novič, M. Multiclass classifier for p-glycoprotein substrates, inhibitors, and non-active compounds. Molecules, 2019, 24(10), 2006.
[http://dx.doi.org/10.3390/molecules24102006] [PMID: 31130601]
[47]
Mora Lagares, L.; Pérez-Castillo, Y.; Minovski, N.; Novič, M. Structure–function relationships in the human p-glycoprotein (ABCB1): Insights from molecular dynamics simulations. Int. J. Mol. Sci., 2021, 23(1), 362.
[http://dx.doi.org/10.3390/ijms23010362] [PMID: 35008783]
[48]
Benfenati, E.; Roncaglioni, A.; Lombardo, A.; Manganaro, A. Integrating QSAR, read-across, and screening tools: The VEGAHUB platform as an example. Chall. Adv. Comput. Chem. Phys., 2019, 30, 365-381.
[http://dx.doi.org/10.1007/978-3-030-16443-0_18]
[49]
Mlinšek, G.; Novič, M.; Hodošček, M.; Šolmajer, T. Prediction of enzyme binding: Human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1286-1294.
[http://dx.doi.org/10.1021/ci000162e] [PMID: 11604028]
[50]
Mlinšek, G.; Novič, M.; Kotnik, M.; Šolmajer, T. Enzyme binding selectivity prediction: Alpha-thrombin vs trypsin inhibition. J. Chem. Inf. Comput. Sci., 2004, 44(5), 1872-1882.
[http://dx.doi.org/10.1021/ci0401017] [PMID: 15446847]
[51]
Župerl, Š.; Mlinšek, G.; Šolmajer, T.; Zupan, J.; Novič, M. Prediction of binding affinities of thrombin and trypsin inhibitors by chemometric modeling. J. Chemometr., 2007, 21(7-9), 346-356.
[http://dx.doi.org/10.1002/cem.1046]
[52]
Minovski, N.; Perdih, A.; Novič, M.; Šolmajer, T. Cluster-based molecular docking study for in silico identification of novel 6-fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis. J. Comput. Chem., 2013, 34(9), 790-801.
[http://dx.doi.org/10.1002/jcc.23205] [PMID: 23280926]
[53]
Minovski, N.; Perdih, A.; Šolmajer, T. Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: A chemometric and molecular modeling assessment. J. Mol. Model., 2012, 18(5), 1735-1753.
[http://dx.doi.org/10.1007/s00894-011-1179-0] [PMID: 21833830]
[54]
Minovski, N.; Vračko, M.; Šolmajer, T. Quantitative structure–activity relationship study of antitubercular fluoroquinolones. Mol. Divers., 2011, 15(2), 417-426.
[http://dx.doi.org/10.1007/s11030-010-9238-5] [PMID: 20229318]
[55]
Borišek, J.; Drgan, V.; Minovski, N.; Novič, M. Mechanistic interpretation of artificial neural network-based QSAR model for prediction of cathepsin K inhibition potency. J. Chemometr., 2014, 28(4), 272-281.
[http://dx.doi.org/10.1002/cem.2617]
[56]
Borišek, J.; Vizovišek, M.; Sosnowski, P.; Turk, B.; Turk, D.; Mohar, B.; Novič, M. Development of N -(Functionalized benzoyl)-homocycloleucyl-glycinonitriles as potent cathepsin k inhibitors. J. Med. Chem., 2015, 58(17), 6928-6937.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00746] [PMID: 26280490]
[57]
Minovski, N.; Župerl, Š.; Drgan, V.; Novič, M. Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study. Anal. Chim. Acta, 2013, 759, 28-42.
[http://dx.doi.org/10.1016/j.aca.2012.11.002] [PMID: 23260674]
[58]
Bolčič-Tavčar, M.; Vračko, M. Prediction of mutagenicity, carcinogenicity, developmental toxicity, and skin sensitisation with Caesar program for a set of conazoles. Arh. Hig. Rada Toksikol., 2012, 63(3), 283-292.
[http://dx.doi.org/10.2478/10004-1254-63-2012-2188] [PMID: 23152378]
[59]
Plošnik, A.; Zupan, J.; Vračko, M. Evaluation of toxic endpoints for a set of cosmetic ingredients with CAESAR models. Chemosphere, 2015, 120, 492-499.
[http://dx.doi.org/10.1016/j.chemosphere.2014.09.013] [PMID: 25278177]
[60]
Vračko, M.; Bobst, S. Performance evaluation of CAESAR-QSAR output using PAHs as a case study. J. Chemometr., 2014, 28(2), 100-107.
[http://dx.doi.org/10.1002/cem.2578]
[61]
Novič, M.; Vračko, M. QSAR models for reproductive toxicity and endocrine disruption activity. Molecules, 2010, 15(3), 1987-1999.
[http://dx.doi.org/10.3390/molecules15031987] [PMID: 20336027]
[62]
Devillers, J. Endocrine Disruption Modeling, CRC Press, 2009.
[63]
Novič, M.; Vračko, M. Kohonen and Counterpropagation Neural Networks Employed for Modeling Endocrine Disruptors. Endocrine Disruption Modeling; Devillers, J., Ed.; CRC Press, 2009.
[64]
Marini, F.; Roncaglioni, A.; Novič, M. Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders. J. Chem. Inf. Model., 2005, 45(6), 1507-1519.
[http://dx.doi.org/10.1021/ci0501645] [PMID: 16309247]
[65]
Roncaglioni, A.; Novič, M.; Vračko, M.; Benfenati, E. Classification of potential endocrine disrupters on the basis of molecular structure using a nonlinear modeling method. J. Chem. Inf. Comput. Sci., 2004, 44(2), 300-309.
[http://dx.doi.org/10.1021/ci030421a] [PMID: 15032504]
[66]
Boriani, E.; Spreafico, M.; Benfenati, E.; Novič, M. Structural features of diverse ligands influencing binding affinities to estrogen α and estrogen β receptors. Part I: Molecular descriptors calculated from minimal energy conformation of isolated ligands. Mol. Divers., 2007, 11(3-4), 153-169.
[http://dx.doi.org/10.1007/s11030-008-9069-9] [PMID: 18320337]
[67]
Spreafico, M.; Boriani, E.; Benfenati, E.; Novič, M. Structural features of diverse ligands influencing binding affinities to estrogen α and estrogen β receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study. Mol. Divers., 2007, 11(3-4), 171-181.
[http://dx.doi.org/10.1007/s11030-008-9070-3] [PMID: 18317942]
[68]
Stojić, N.; Erić, S.; Kuzmanovski, I. Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks. J. Mol. Graph. Model., 2010, 29(3), 450-460.
[http://dx.doi.org/10.1016/j.jmgm.2010.09.001] [PMID: 20952233]
[69]
Fjodorova, N.; Vračko, M.; Novič, M.; Roncaglioni, A.; Benfenati, E. New public QSAR model for carcinogenicity. Chem. Cent. J., 2010, 4(S1)(1), S3.
[http://dx.doi.org/10.1186/1752-153X-4-S1-S3] [PMID: 20678182]
[70]
Fjodorova, N.; Vračko, M.; Jezierska, A.; Novič, M. Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals. SAR QSAR Environ. Res., 2010, 21(1-2), 57-75.
[http://dx.doi.org/10.1080/10629360903563250] [PMID: 20373214]
[71]
Fjodorova, N.; Novič, M. Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network. J. Comput. Aided Mol. Des., 2011, 25(12), 1159-1169.
[http://dx.doi.org/10.1007/s10822-011-9500-7] [PMID: 22139476]
[72]
Fjodorova, N.; Novič, M. Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree. SAR QSAR Environ. Res., 2014, 25(6), 423-441.
[http://dx.doi.org/10.1080/1062936X.2014.898687] [PMID: 24716754]
[73]
Fjodorova, N.; Novič, M.; Roncaglioni, A.; Benfenati, E. Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network. J. Comput. Aided Mol. Des., 2011, 25(12), 1147-1158.
[http://dx.doi.org/10.1007/s10822-011-9499-9] [PMID: 22139475]
[74]
Fjodorova, N.; Novič, M.; Župerl, Š.; Venko, K. Counter-propagation artificial neural network models for prediction of carcinogenicity of non-congeneric chemicals for regulatory uses, advances in qsar modeling: Applications in pharmaceutical, chemical, food. Agricul. Environ. Sci., 2017, 24, 503-527.
[75]
Fjodorova, N.; Vračko, M.; Tušar, M.; Jezierska, A.; Novič, M.; Kühne, R.; Schüürmann, G. Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol. Divers., 2010, 14(3), 581-594.
[http://dx.doi.org/10.1007/s11030-009-9190-4] [PMID: 19685274]
[76]
Fjodorova, N.; Novič, M. Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity. Comput. Struct. Biotechnol. J., 2012, 1(2), e201207003.
[http://dx.doi.org/10.5936/csbj.201207003] [PMID: 24688639]
[77]
Chalasani, N.; Björnsson, E. Risk factors for idiosyncratic drug-induced liver injury. Gastroenterology, 2010, 138(7), 2246-2259.
[http://dx.doi.org/10.1053/j.gastro.2010.04.001] [PMID: 20394749]
[78]
Bajželj, B.; Drgan, V. Hepatotoxicity modeling using counter-propagation artificial neural networks: Handling an imbalanced classification problem. Molecules, 2020, 25(3), 481.
[http://dx.doi.org/10.3390/molecules25030481] [PMID: 31979300]
[79]
Drgan, V.; Bajželj, B. Application of supervised som algorithms in predicting the hepatotoxic potential of drugs. Int. J. Mol. Sci., 2021, 22(9), 4443.
[http://dx.doi.org/10.3390/ijms22094443] [PMID: 33923145]
[80]
Fourches, D.; Pu, D.; Tassa, C.; Weissleder, R.; Shaw, S.Y.; Mumper, R.J.; Tropsha, A. Quantitative nanostructure-activity relationship modeling. ACS Nano, 2010, 4(10), 5703-5712.
[http://dx.doi.org/10.1021/nn1013484] [PMID: 20857979]
[81]
Mu, Q.; Jiang, G.; Chen, L.; Zhou, H.; Fourches, D.; Tropsha, A.; Yan, B. Chemical basis of interactions between engineered nanoparticles and biological systems. Chem. Rev., 2014, 114(15), 7740-7781.
[http://dx.doi.org/10.1021/cr400295a] [PMID: 24927254]
[82]
Fjodorova, N.; Novič, M.; Gajewicz, A.; Rasulev, B. The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method. Nanotoxicology, 2017, 11(4), 475-483.
[http://dx.doi.org/10.1080/17435390.2017.1310949] [PMID: 28330416]
[83]
Puzyn, T.; Rasulev, B.; Gajewicz, A.; Hu, X.; Dasari, T.P.; Michalkova, A.; Hwang, H.M.; Toropov, A.; Leszczynska, D.; Leszczynski, J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat. Nanotechnol., 2011, 6(3), 175-178.
[http://dx.doi.org/10.1038/nnano.2011.10] [PMID: 21317892]
[84]
Gajewicz, A.; Cronin, M.T.D.; Rasulev, B.; Leszczynski, J.; Puzyn, T. Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: Nano-read-across. Nanotechnology, 2015, 26(1), 015701.
[http://dx.doi.org/10.1088/0957-4484/26/1/015701] [PMID: 25473798]
[85]
Gajewicz, A.; Schaeublin, N.; Rasulev, B.; Hussain, S.; Leszczynska, D.; Puzyn, T.; Leszczynski, J. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies. Nanotoxicology, 2015, 9(3), 313-325.
[http://dx.doi.org/10.3109/17435390.2014.930195] [PMID: 24983896]
[86]
Fjodorova, N.; Novič, M.; Venko, K.; Rasulev, B. A comprehensive cheminformatics analysis of structural features affecting the binding activity of fullerene derivatives. Nanomaterials, 2020, 10(1), 90.
[http://dx.doi.org/10.3390/nano10010090] [PMID: 31906497]
[87]
Fjodorova, N.; Novič, M.; Venko, K.; Drgan, V.; Rasulev, B.; Türker Saçan, M.; Sağ Erdem, S.; Tugcu, G.; Toropova, A.P.; Toropov, A.A. How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases. Comput. Struct. Biotechnol. J., 2022, 20, 913-924.
[http://dx.doi.org/10.1016/j.csbj.2022.02.006] [PMID: 35242284]
[88]
Papa, E.; Villa, F.; Gramatica, P. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow). J. Chem. Inf. Model., 2005, 45(5), 1256-1266.
[http://dx.doi.org/10.1021/ci050212l] [PMID: 16180902]
[89]
Vračko, M.; Bandelj, V.; Barbieri, P.; Benfenati, E.; Chaudhry, Q.; Cronin, M.; Devillers, J.; Gallegos, A.; Gini, G.; Gramatica, P.; Helma, C.; Mazzatorta, P.; Neagu, D.; Netzeva, T.; Pavan, M.; Patlewicz, G.; Randić, M.; Tsakovska, I.; Worth, A. Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: A case study. SAR QSAR Environ. Res., 2006, 17(3), 265-284.
[http://dx.doi.org/10.1080/10659360600787650] [PMID: 16815767]
[90]
Drgan, V.; Župerl, Š.; Vračko, M.; Como, F.; Novič, M. Robust modelling of acute toxicity towards fathead minnow ( Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ. Res., 2016, 27(7), 501-519.
[http://dx.doi.org/10.1080/1062936X.2016.1196388] [PMID: 27322761]
[91]
Roy, K. Chemometrics and Cheminformatics in Aquatic Toxicology; John Wiley & Sons, Inc, 2021.
[92]
Benfenati, E.; Lombardo, A.; Drgan, V.; Novič, M.; Manganaro, A. The tools for aquatic toxicology within the VEGAHUB system.Chemometrics and Cheminformatics in Aquatic Toxicology; Roy, K., Ed.; John Wiley & Sons, Inc., 2021.
[http://dx.doi.org/10.1002/9781119681397.ch25]
[93]
Manganelli, S.; Gamba, A.; Colombo, E.; Benfenati, E. Using VEGAHUB within a weight-of-evidence strategy. Methods Mol. Biol., 2022, 2425, 479-495.
[http://dx.doi.org/10.1007/978-1-0716-1960-5_18] [PMID: 35188643]
[94]
Tugcu, G.; Saçan, M.T.; Vračko, M.; Novič, M.; Minovski, N. QSTR modelling of the acute toxicity of pharmaceuticals to fish. SAR QSAR Environ. Res., 2012, 23(3-4), 297-310.
[http://dx.doi.org/10.1080/1062936X.2012.657678] [PMID: 22380018]
[95]
Sacan, M.T.; Novič, M.; Ertürk, M.D.; Minovski, N. Marine algal toxicity models with dunaliella tertiolecta: in vivo and in silico.Advances in Mathematical Chemistry and Applications; Basak, S.C.; Restrepo, G.; Villaveces, J.L., Eds.; Bentham Science Publishers, 2014.
[96]
Minovski, N.; Saçan, M.T.; Eminoğlu, E.M.; Erdem, S.S.; Novič, M. Revisiting fish toxicity of active pharmaceutical ingredients: Mechanistic insights from integrated ligand-/structure-based assessments on acetylcholinesterase. Ecotoxicol. Environ. Saf., 2019, 170, 548-558.
[http://dx.doi.org/10.1016/j.ecoenv.2018.11.099] [PMID: 30572250]
[97]
Venko, K.; Drgan, V.; Novič, M. Classification models for identifying substances exhibiting acute contact toxicity in honeybees ( Apis mellifera ) $. SAR QSAR Environ. Res., 2018, 29(9), 743-754.
[http://dx.doi.org/10.1080/1062936X.2018.1513953] [PMID: 30220217]
[98]
Hardy, B.; Douglas, N.; Helma, C.; Rautenberg, M.; Jeliazkova, N.; Jeliazkov, V.; Nikolova, I.; Benigni, R.; Tcheremenskaia, O.; Kramer, S.; Girschick, T.; Buchwald, F.; Wicker, J.; Karwath, A.; Gutlein, M.; Maunz, A.; Sarimveis, H.; Melagraki, G.; Afantitis, A.; Sopasakis, P.; Gallagher, D.; Poroikov, V.; Filimonov, D.; Zakharov, A.; Lagunin, A.; Gloriozova, T.; Novikov, S.; Skvortsova, N.; Druzhilovsky, D.; Chawla, S.; Ghosh, I.; Ray, S.; Patel, H.; Escher, S. Collaborative development of predictive toxicology applications. J. Cheminform., 2010, 2.
[99]
Maertens, A.; Golden, E.; Luechtefeld, T.H.; Hoffmann, S.; Tsaioun, K.; Hartung, T. Probabilistic risk assessment : The keystone for the future of toxicology. Altern. Anim. Exp., 2022, 39(1), 3-29.
[http://dx.doi.org/10.14573/altex.2201081] [PMID: 35034131]

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