[1]
Amin, S.A.; Adhikari, N.; Baidya, S.K.; Gayen, S.; Jha, T. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J. Biomol. Struct. Dyn., 2018, 1-20.
[2]
Toropova, A.P.; Toropov, A.A. CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Comput. Biol. Chem., 2018, 72, 26-32.
[3]
Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Leszczynska, D.; Leszczynski, J. Semi-correlations combined with the Index of Ideality of Correlation: A tool to build up model of mutagenic potential. Mol. Cell. Biochem., 2018, 452(1-2), 1-8.
[4]
Sokolović, D.; Ranković, J.; Stanković, V.; Stefanović, R.; Karaleić, S.; Mekić, B.; Milenković, V.; Kocić, J.; Veselinović, A.M. QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med. Chem. Res., 2017, 26, 796-804.
[5]
Sokolović, D.; Stanković, V.; Toskić, D.; Lilić, L.; Ranković, G.; Ranković, J.; Nedin-Ranković, G.; Veselinović, A.M. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct. Chem., 2016, 27, 1511-1519.
[6]
Islam, M.A.; Pillay, T.S. Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemom. Intell. Lab. Syst., 2016, 153, 67-74.
[7]
Živković, J.V.; Trutić, N.V.; Veselinović, J.B.; Nikolić, G.M.; Veselinović, A.M. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. Comput. Biol. Med., 2015, 64, 276-282.
[8]
Veselinović, A.M.; Veselinović, J.B.; Živković, J.V.; Nikolić, G.M. Application of smiles notation based optimal descriptors in drug discovery and design. Curr. Top. Med. Chem., 2015, 15, 1768-1779.
[9]
Begum, S.; Achary, P.G.R. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1). SAR QSAR Environ. Res., 2015, 26, 343-361.
[10]
Veselinović, J.B.; Nikolić, G.M.; Trutić, N.V.; Živković, J.V.; Veselinović, A.M. Monte Carlo QSAR models for predicting organophosphate inhibition of acetylcholinesterase. SAR QSAR Environ. Res., 2015, 26, 449-460.
[11]
Fatemi, M.H.; Malekzadeh, H. CORAL: Predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches. J. Iran. Chem. Soc, 2015, 12, 405-412.
[12]
Li, Q.; Ding, X.; Si, H.; Gao, H. QSAR model based on SMILES of inhibitory rate of 2,3-diarylpropenoic acids on AKR1C3. Chemom. Intell. Lab. Syst., 2014, 139, 132-138.
[13]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. Lett. Drug Des. Discov., 2014, 11, 420-427.
[14]
Kumar, A.; Chauhan, S. Use of simplified molecular input line entry system and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future Med. Chem., 2018, 10(13), 1603-1622.
[15]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer. Agents Med. Chem., 2013, 13, 791-800.
[16]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents. Anticancer. Agents Med. Chem., 2012, 12, 807-817.
[17]
Kleandrova, V.V.; Luan, F.; Speck-Planche, A.; Cordeiro, M.N.D.S. In silico assessment of the acute toxicity of chemicals: Recent advances and new model for multitasking prediction of toxic effect. Mini Rev. Med. Chem., 2015, 15, 677-686.
[18]
Speck-Planche, A.; Cordeiro, M.N.D.S. A general ann-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol. Biol., 2015, 1260, 45-64.
[19]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multi-target QSAR approaches for modeling protein inhibitors. Simultaneous prediction of activities against biomacromolecules present in gram-negative bacteria. Curr. Top. Med. Chem., 2015, 15, 1801-1813.
[20]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multitasking models for quantitative structure-biological effect relationships: Current status and future perspectives to speed up drug discovery. Expert Opin. Drug Discov., 2015, 10, 245-256.
[21]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Computational modeling in nanomedicine: Prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine., 2015, 10, 193-204.
[22]
Scotti, L.; Scotti, M.T. In silico studies applied to natural products with potential activity against Alzheimer’s disease. Neuromethods, 2018, 132, 513-531.
[23]
Scotti, M.T.; Scotti, L.; Ishiki, H.M.; Peron, L.M.; de Rezende, L.; do Amaral, A.T. Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues. Chemom. Intell. Lab. Syst., 2016, 154, 137-149.
[24]
Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T.; Cordeiro, M.N.D.S. 3D-QSAR methodologies and molecular modeling in bioinformatics for the search of novel anti-HIV therapies: Rational design of entry inhibitors. Curr. Bioinform., 2013, 8(4), 452-464.
[25]
Toropova, A.P.; Toropov, A.A.; Beeg, M.; Gobbi, M.; Salmona, M. Utilization of the monte carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides. Curr. Drug Discov. Technol., 2017, 14(4), 229-243.
[26]
Duchowicz, P.R.; Bacelo, D.E.; Fioressi, S.E.; Palermo, V.; Ibezim, N.E.; Romanelli, G.P. QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Med. Chem. Res., 2018, 27(2), 420-428.
[27]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Salmona, M. Mutagenicity, anticancer activity, and blood brain barrier: Similarity and dissimilarity of molecular alerts. Toxicol. Mech. Methods, 2018, 28(5), 321-327.
[28]
Toropov, A.A.; Toropova, A.P.; Raska, I.; Leszczynska, D.; Leszczynski, J. Comprehension of drug toxicity: Software and databases. Comput. Biol. Med., 2014, 45, 20-25.
[31]
Toropova, A.P.; Toropov, A.A. CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur. J. Pharm. Sci., 2014, 52, 21-25.
[32]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. SMILES-based QSAR approaches for carcinogenicity and anticancer activity: Comparison of correlation weights for identical SMILES attributes. Anticancer. Agents Med. Chem., 2011, 11, 974-982.
[33]
Toropova, A.P.; Toropov, A.A.; Diaza, R.G.; Benfenati, E.; Gini, G. Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: An unexpected good prediction based on a model that seems untrustworthy. Cent. Eur. J. Chem., 2011, 9, 165-174.
[34]
Toropov, A.A.; Toropova, A.P.; Benfenati, E. SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes. Eur. J. Med. Chem., 2010, 45, 3581-3587.
[35]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Manganaro, A. QSAR modelling of carcinogenicity by balance of correlations. Mol. Divers., 2009, 13, 367-373.
[36]
Toropov, A.A.; Toropova, A.P.; Benfenati, E. Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions. Int. J. Mol. Sci., 2009, 10, 3106-3127.
[37]
Song, F.; Zhang, A.; Liang, H.; Cui, L.; Li, W.; Si, H.; Duan, Y.; Zhai, H. QSAR study for carcinogenic potency of aromatic amines based on GEP and MLPs. I. J. Environ. Res. Public Health, 2016, 13(11), 1141.
[38]
Harding, A.P.; Popelier, P.L.A.; Harvey, J.; Giddings, A.; Foster, G.; Kranz, M. Evaluation of aromatic amines with different purities and different solvent vehicles in the Ames test. Regul. Toxicol. Pharmacol., 2015, 71, 244-250.
[39]
Garrigós, M.C.; Reche, F.; Marín, M.L.; Pernías, K.; Jiménez, A. Optimization of the extraction of azo colorants used in toy products. J. Chromatogr. A, 2002, 963, 427-433.
[40]
Sanchis, Y.; Coscollà, C.; Roca, M.; Yusà, V. Target analysis of primary aromatic amines combined with a comprehensive screening of migrating substances in kitchen utensils by liquid chromatography-high resolution mass spectrometry. Talanta, 2015, 138, 290-297.
[41]
Petrescu, A-M.; Ilia, G. Molecular docking study to evaluate the carcinogenic potential and mammalian toxicity of thiophosphonate pesticides by cluster and discriminant analysis. Environ. Toxicol. Pharmacol., 2016, 47, 62-78.
[42]
Toropov, A.A.; Toropova, A.P. The Index of Ideality of Correlation:
A criterion of predictive potential of QSPR/QSAR models? Mut. Res. Gen. Tox. En. Mut., 2017, 819, 31-37.
[43]
Toropova, A.P.; Toropov, A.A. CORAL: Monte carlo method to predict endpoints for medical chemistry. Mini Rev. Med. Chem., 2018, 18(5), 382-391.
[44]
Toropov, A.A.; Toropova, A.P.; Raitano, G.; Benfenati, E. CORAL: Building up QSAR models for the chromosome aberration test. Saudi J. Biol. Sci., 2018, 40(2)
[46]
Toropov, A.A.; Toropova, A.P. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol. Mech. Methods, 2018, 29(1), 1-23.
[47]
Bouhedjar, K.; Manganelli, S.; Gini, G.; Toropov, A.A.; Toropova, A.P.; Ali-Mokhnache, S.; Messadi, D. QSAR modeling useful in anti-cancer drug discovery: Prediction of V600EBRAF-Dependent P-ERK using monte carlo method. J. Med. Chem. Toxicol, 2017, 2(1), 1-6.
[48]
Toropova, M.A.; Raska, Jr, I.; Toropova, A.P.; Raskova, M. CORAL software: Analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors. Curr. Drug Metab., 2017, 18(6), 500-510.
[49]
Toropova, M.A. Drug metabolism as an object of computational analysis by the monte carlo method. Curr. Drug Metab., 2017, 18(12), 1123-1131.
[50]
Pradeep, P.; Povinelli, R.J.; White, S.; Merrill, S.J. An ensemble model of QSAR tools for regulatory risk assessment. J. Cheminform., 2016, 8, 1-9.
[51]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G. OCWLGI Descriptors: Theory and praxis. Curr. Comput. Aid. Drug Des, 2013, 9, 226-232.
[52]
Weininger, D.; Weininger, A.; Weininger, J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci., 1989, 29, 97-101.
[53]
Toropova, A.P.; Toropov, A.A.; Martyanov, S.E.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynksy, J. CORAL: Monte carlo method as a tool for the prediction of the bioconcentration factor of industrial pollutants. Mol. Inform., 2013, 32, 145-154.
[54]
Toropov, A.A.; Toropova, A.P.; Martyanov, S.E.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynksy, J. CORAL: Predictions of rate constants of hydroxyl radical reaction using representation of the molecular structure obtained by combination of SMILES and graph approaches. Chemom. Intell. Lab. Syst., 2012, 112, 65-70.
[56]
Toropov, A.A.; Toropova, A.P.; Voropaeva, N.L.; Ruban, I.N.; Rashidova, S.S.H. Approval of the random-mutual-orientation statistics index as a basis for searching for “structure property” relationships in coordination compounds. Russ. J. Coord. Chem., 1996, 22, 578-580.
[57]
Toropov, A.A.; Toropova, A.P.; Ismailov, T.T.; Voropaeva, N.L.; Ruban, I.N.; Rashidova, S.S.H. The use of deformation indices of the ideal symmetry model in calculations of the thermodynamic properties of organic compounds. Russ. J. Phys. Chem. A, 1996, 70, 1081-1084.
[58]
Garkani-Nejad, Z.; Shahhoseini, M. Prediction of the anti-cancer activity of spiro derivatives of parthenin based on molecular modeling methods and docking. Med. Chem. Res., 2014, 23, 3403-3417.
[59]
Pogorzelska, A.; Slawinski, J.; Brozewicz, K.; Ulenberg, S.; Baczek, T. Novel 3-amino-6-chloro-7-(azol-2 or 5-yl)-1,1-dioxo-1,4,2-benzodithiazine derivatives with anticancer activity: Synthesis and QSAR study. Molecules, 2015, 20, 21960-21970.
[60]
Qian, J-Z.; Wang, B-C.; Fan, Y.; Tan, J.; Yang, X. QSAR study of flavonoid-metal complexes and their anticancer activities. J. Struct. Chem., 2015, 56, 338-345.
[61]
Ghanbari, Z.; Housaindokht, M.R.; Izadyar, M.; Bozorgmehr, M.R.; Eshtiagh-Hosseini, H.; Bahrami, A.R.; Matin, M.M.; Khoshkholgh, M.J. Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents. Sci. World J., 2014, 745649.
[62]
Ivkovic, B.M.; Nikolic, K.; Ilic, B.B.; Žižak, Ž.S.; Novakovic, R.B.; Cudina, O.A.; Vladimirov, S.M. Phenylpropiophenone derivatives as potential anticancer agents: Synthesis, biological evaluation and quantitative structure-activity relationship study. Eur. J. Med. Chem., 2013, 63, 239-255.
[63]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines exhibiting anticancer activity by optimal SMILES-based descriptors. J. Math. Chem., 2010, 47, 647-666.
[64]
Benfenati, E.; Toropov, A.A.; Toropova, A.P.; Manganaro, A.; Gonella Diaza, R. CORAL software: QSAR for anticancer agents. Chem. Biol. Drug Des., 2011, 77, 471-476.
[65]
Worachartcheewan, A.; Mandi, P.; Prachayasittikul, V.; Toropova, A.P.; Toropov, A.A.; Nantasenamat, C. Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemom. Intell. Lab. Syst., 2014, 138, 120-126.
[66]
Trinh, T.X.; Choi, J.S.; Jeon, H.; Byun, H.G.; Yoon, T.H.; Kim, J. Quasi-SMILES-based nano-quantitative structure-activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol., 2018, 31(3), 183-190.
[67]
OECD, 2007. (Organization for Economic Co-operation and Development).
guidance document on the validation of (quantitative)
structure-activity relationship [(Q)SAR] Models No. 69.
[68]
Toropova, M.A.; Raška, I., Jr; Toropov, A.A.; Rašková, M. The utilization of the Monte Carlo technique for rational drug discovery. Comb. Chem. High Throughput Screen., 2016, 19(8), 676-687.
[70]
Lebedeva, G.; Sorokin, A.; Faratian, D.; Mullen, P.; Goltsov, A.; Langdon, S.P.; Harrison, D.J.; Goryanin, I. Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network. Eur. J. Pharm. Sci., 2012, 46, 244-258.
[71]
Hettle, R.; Posnett, J.; Borrill, J. Challenges in economic modeling of anticancer therapies: An example of modeling the survival benefit of olaparib maintenance therapy for patients with BRCA-mutated platinum-sensitive relapsed ovarian cancer. J. Med. Econ., 2015, 18, 516-524.
[72]
Afantitis, A.; Melagraki, G.; Sarimveis, H.; Koutentis, P.A.; Markopoulos, J.; Igglessi-Markopoulou, O. Development and evaluation of a QSPR model for the prediction of diamagnetic susceptibility. QSAR Comb. Sci., 2008, 27, 432-436.
[73]
Melagraki, G.; Ntougkos, E.; Rinotas, V.; Papaneophytou, C.; Leonis, G.; Mavromoustakos, T.; Kontopidis, G.; Douni, E.; Afantitis, A.; Kollias, G. Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLOS Comput. Biol., 2017, 13, e1005372.
[74]
Zhang, S.; Golbraikh, A.; Oloff, S.; Kohn, H.; Tropsha, A. A novel automated lazy learning QSAR (ALL-QSAR) approach: Method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model., 2006, 46, 1984-1995.
[75]
Melagraki, G.; Afantitis, A. A risk assessment tool for the virtual screening of metal oxide nanoparticles through enalos in silico nano platform. Curr. Top. Med. Chem., 2015, 15, 1827-1836.
[76]
Toropova, A.P.; Toropov, A.A.; Veselinović, J.B.; Veselinović, A.M. QSAR as a random event: A case of NOAEL. Environ. Sci. Pollut. Res. Int., 2015, 22, 8264-8271.
[77]
Toropova, A.P.; Toropov, A.A.; Rallo, R.; Leszczynska, D.; Leszczynski, J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol. Environ. Saf., 2015, 112, 39-45.