References
- Achary, P. G. R., Toropova, A. P., & Toropov, A. A. (2019). Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness. Food Research International (Ottawa, Ont.), 122, 40–46. https://doi.org/https://doi.org/10.1016/j.foodres.2019.03.067
- Ahmadi, S. (2020). Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere, 242, 125192. https://doi.org/https://doi.org/10.1016/j.chemosphere.2019.125192
- Ahmadi, S., Ghanbari, H., Lotfi, S., & Azimi, N. (2020). Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method. Molecular Diversity. https://doi.org/https://doi.org/10.1007/s11030-019-10026-9
- Ahmadi, S., Mehrabi, M., Rezaei, S., & Mardafkan, N. (2019). Structure–activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals. Journal of Molecular Structure, 1191, 165–174. https://doi.org/https://doi.org/10.1016/j.molstruc.2019.04.103
- Angelova, V., Karabeliov, V., Andreeva‐Gateva, P. A., & Tchekalarova, J. (2016). Recent developments of hydrazide/hydrazone derivatives and their analogs as anticonvulsant agents in animal models. Drug Development Research, 77(7), 379–392. https://doi.org/https://doi.org/10.1002/ddr.21329
- Bhatia, R., Kadyan, K., Duhan, M., Devi, M., Singh, R., Kamboj, R. C., & Kumar, P. (2019). A Serendipitous synthesis: SiO2–HNO3 mediated oxidative aromatization and regioselective nitration of 1,3,5-trisubstituted-4,5-dihydro-1H-pyrazoles. ChemistrySelect, 4(35), 10417–10424. https://doi.org/https://doi.org/10.1002/slct.201902285
- Bhutani, R., Pathak, D. P., Kapoor, G., Husain, A., Kant, R., & Iqbal, M. A. (2018). Synthesis, molecular modelling studies and ADME prediction of benzothiazole clubbed oxadiazole-Mannich bases, and evaluation of their anti-diabetic activity through in vivo model. Bioorganic Chemistry, 77, 6–15. https://doi.org/https://doi.org/10.1016/j.bioorg.2017.12.037
- Brayer, G. D., Luo, Y., & Withers, S. G. (1995). The structure of human pancreatic alpha-amylase at 1.8 A resolution and comparisons with related enzymes . Protein Science: A Publication of the Protein Society, 4(9), 1730–1742. https://doi.org/https://doi.org/10.1002/pro.5560040908
- Cardoso, L. N. F., Nogueira, T. C. M., Rodrigues, F. A. R., Oliveira, A. C. A., dos Santos Luciano, M. C., Pessoa, C., & de Souza, M. V. N. (2017). N-acylhydrazones containing thiophene nucleus: A new anticancer class. Medicinal Chemistry Research, 26(8), 1605–1608. https://doi.org/https://doi.org/10.1007/s00044-017-1832-y
- Duhan, M., Singh, R., Devi, M., Sindhu, J., Bhatia, R., Kumar, A., & Kumar, P. (2019). Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. Journal of Biomolecular Structure and Dynamics. 1–31. https://doi.org/https://doi.org/10.1080/07391102.2019.1704885
- Dunbrack, R. L. Jr. (2002). Rotamer libraries in the 21st century. Current Opinion in Structural Biology, 12(4), 431–440. https://doi.org/https://doi.org/10.1016/S0959-440X(02)00344-5
- Ferreira, L. G., Dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015). Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 20(7), 13384–13421. https://doi.org/https://doi.org/10.3390/molecules200713384
- Gollapalli, M., Taha, M., Javid, M. T., Almandil, N. B., Rahim, F., Wadood, A., Mosaddik, A., Ibrahim, M., Alqahtani, M. A., & Bamarouf, Y. A. (2019). Synthesis of benzothiazole derivatives as a potent α-glucosidase inhibitor. Bioorganic Chemistry, 85, 33–48. https://doi.org/https://doi.org/10.1016/j.bioorg.2018.12.021
- Guo, Z., Mohanty, U., Noehre, J., Sawyer, T. K., Sherman, W., & Krilov, G. (2010). Probing the alpha-helical structural stability of stapled p53 peptides: Molecular dynamics simulations and analysis. Chemical Biology & Drug Design, 75(4), 348–359. https://doi.org/https://doi.org/10.1111/j.1747-0285.2010.00951.x
- Hernández-Vázquez, E., Salgado-Barrera, S., Ramírez-Espinosa, J. J., Estrada-Soto, S., & Hernández-Luis, F. (2016). Synthesis and molecular docking of N′-arylidene-5-(4-chlorophenyl)-1-(3,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carbohydrazides as novel hypoglycemic and antioxidant dual agents . Bioorganic & Medicinal Chemistry, 24(10), 2298–2306. https://doi.org/https://doi.org/10.1016/j.bmc.2016.04.007
- Hossain, K. A., & Roy, K. (2018). Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches. Ecotoxicology and Environmental Safety, 166, 92–101. https://doi.org/https://doi.org/10.1016/j.ecoenv.2018.09.068
- Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/https://doi.org/10.1063/1.445869
- Karelson, M., Lobanov, V. S., & Katritzky, A. R. (1996). Quantum-chemical descriptors in QSAR/QSPR Studies. Chemical Reviews, 96(3), 1027–1044. https://doi.org/https://doi.org/10.1021/cr950202r
- Kaur, I., Khajuria, A., Ohri, P., Kaur, P., & Singh, K. (2018). Benzothiazole based Schiff-base-A mechanistically discrete sensor for HSO4− and I−: Application to bioimaging and vapour phase sensing of ethyl acetate. Sensors and Actuators B: Chemical, 268, 29–38. https://doi.org/https://doi.org/10.1016/j.snb.2018.04.072
- Keharom, S., Mahachai, R., & Chanthai, S. (2016). The optimization study of α-amylase activity based on central composite design–response surface methodology by dinitrosalicylic acid method. International Food Research Journal, 23(1), 10–17.
- Khan, K., Benfenati, E., & Roy, K. (2019a). Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds. Ecotoxicology and Environmental Safety, 168, 287–297. https://doi.org/https://doi.org/10.1016/j.ecoenv.2018.10.060
- Khan, P. M., Roy, K., & Benfenati, E. (2019b). Chemometric modeling of Daphnia magna toxicity of agrochemicals. Chemosphere, 224, 470–479. https://doi.org/https://doi.org/10.1016/j.chemosphere.2019.02.147
- Krallinger, M., Rabal, O., Lourenco, A., Oyarzabal, J., & Valencia, A. (2017). Information retrieval and text mining technologies for chemistry. Chemical Reviews, 117(12), 7673–7761. https://doi.org/https://doi.org/10.1021/acs.chemrev.6b00851
- Kumar, A., & Chauhan, S. (2017). Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors. Archiv der Pharmazie (Weinheim), 350(1), e1600268. https://doi.org/https://doi.org/10.1002/ardp.201600268
- Kumar, P., Bhatia, R., Kumar, D., Kamboj, R. C., Kumar, S., Kamal, R., & Kumar, R. (2015). An economic, simple and convenient synthesis of 2-aryl/heteroaryl/styryl/alkylbenzothiazoles using SiO2–HNO3. Research on Chemical Intermediates, 41(7), 4283–4292. https://doi.org/https://doi.org/10.1007/s11164-013-1529-x
- Kumar, P., Duhan, M., Kadyan, K., Bhardwaj, J. K., Saraf, P., & Mittal, M. (2018). Multicomponent synthesis of some molecular hybrid containing thiazole pyrazole as apoptosis inducer. Drug Research, 68(2), 72–79. https://doi.org/https://doi.org/10.1055/s-0043-116947
- Kumar, P., Duhan, M., Kadyan, K., Sindhu, J., Kumar, S., & Sharma, H. (2017a). Synthesis of novel inhibitors of α-amylase based on the thiazolidine-4-one skeleton containing a pyrazole moiety and their configurational studies. MedChemComm, 8(7), 1468–1476. https://doi.org/https://doi.org/10.1039/c7md00080d
- Kumar, P., Kadyan, K., Duhan, M., Sindhu, J., Singh, V., & Saharan, B. S. (2017b). Design, synthesis, conformational and molecular docking study of some novel acyl hydrazone based molecular hybrids as antimalarial and antimicrobial agents. Chemistry Central Journal, 11(1), 115. https://doi.org/https://doi.org/10.1186/s13065-017-0344-7
- Kumar, P., & Kumar, A. (2019). Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method. Journal of Biomolecular Structure and Dynamics, 38(11), 3296–3306. https://doi.org/https://doi.org/10.1080/07391102.2019.1656109
- Kumar, P., & Kumar, A. (2020). CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index. Chemometrics and Intelligent Laboratory Systems, 200, 103982. https://doi.org/https://doi.org/10.1016/j.chemolab.2020.103982
- Kumar, P., Kumar, A., & Sindhu, J. (2019a). Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR and QSAR in Environmental Research, 30(2), 63–80. https://doi.org/https://doi.org/10.1080/1062936X.2018.1564067
- Kumar, P., Kumar, A., & Sindhu, J. (2019b). In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR and QSAR in Environmental Research, 30(8), 525–541. https://doi.org/https://doi.org/10.1080/1062936X.2019.1629998
- Kumar, P., Kumar, A., Sindhu, J., & Lal, S. (2019c). QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method. Drug Research, 69(3), 159–167. https://doi.org/https://doi.org/10.1055/a-0652-5290
- Kumar, S., Rathore, D. S., Garg, G., Khatri, K., Saxena, R., & Sahu, S. K. (2017c). Synthesis and evaluation of some benzothiazole derivatives as antidiabetic agents. International Journal of Pharmacy and Pharmaceutical Sciences, 9(2), 60–68. https://doi.org/https://doi.org/10.22159/ijpps.2017v9i2.14359
- Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012). Quantitative structure–property relationship modeling of diverse materials properties. Chemical Reviews, 112(5), 2889–2919. https://doi.org/https://doi.org/10.1021/cr200066h
- Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015). Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research, 38(9), 1686–1701. https://doi.org/https://doi.org/10.1007/s12272-015-0640-5
- Manisha, Chauhan, S., Kumar, P., & Kumar, A. (2019). Development of prediction model for fructose-1,6-bisphosphatase inhibitors using the Monte Carlo method. SAR and QSAR in Environmental Research, 30(3), 145–159. https://doi.org/https://doi.org/10.1080/1062936X.2019.1568299
- Martyna, G. J., Klein, M. L., & Tuckerman, M. (1992). Nose–Hoover chains – The canonical ensemble via continuous dynamics. The Journal of Chemical Physics, 97(4), 2635–2643. https://doi.org/https://doi.org/10.1063/1.463940
- Martyna, G. J., Tobias, D. J., & Klein, M. L. (1994). Constant-pressure molecular dynamics algorithms. The Journal of Chemical Physics, 101(5), 4177–4189. https://doi.org/https://doi.org/10.1063/1.467468
- Mishra, V. R., Ghanavatkar, C. W., Mali, S. N., Qureshi, S. I., Chaudhari, H. K., & Sekar, N. (2019). Design, synthesis, antimicrobial activity and computational studies of novel azo linked substituted benzimidazole, benzoxazole and benzothiazole derivatives. Computational Biology and Chemistry, 78, 330–337. https://doi.org/https://doi.org/10.1016/j.compbiolchem.2019.01.003
- Murtuja, S., Shaquiquzzaman, M., & Amir, M. (2018). Design, synthesis, and screening of hybrid benzothiazolyl-oxadiazoles as anticonvulsant agents. Letters in Drug Design & Discovery, 15(4), 398–405. https://doi.org/https://doi.org/10.2174/1570180814666170526154914
- Nickavar, B., & Yousefian, N. (2010). Inhibitory effects of six allium species on α-Amylase enzyme activity. Iranian Journal of Pharmaceutical Research, 8, 53–57.
- Nimbhal, M., Bagri, K., Kumar, P., & Kumar, A. (2020). The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators. Structural Chemistry, 31(2), 831–839. https://doi.org/https://doi.org/10.1007/s11224-019-01468-w
- Ojha, P. K., & Roy, K. (2011). Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection. Chemometrics and Intelligent Laborary Systems, 109(2), 146–161. https://doi.org/https://doi.org/10.1016/j.chemolab.2011.08.007
- Ojha, P. K., & Roy, K. (2018). Development of a robust and validated 2D-QSPR model for sweetness potency of diverse functional organic molecules. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association, 112, 551–562. https://doi.org/https://doi.org/10.1016/j.fct.2017.03.043
- Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera – A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/https://doi.org/10.1002/jcc.20084
- Roy, K. (2015). Application of chemometrics and cheminformatics in antimalarial drug research. Combinatorial Chemistry & High Throughput Screening, 18(2), 89–90. https://doi.org/https://doi.org/10.2174/138620731802150215154014
- Sharma, A. K., Sharma, R., & Gangwal, A. (2018). Surface tension studies of ternary system: Cu(II) surfactants-2-amino-6-methyl benzothiazole complex plus methanol plus benzene at 311 K. Current Physical Chemistry, 8(2), 151–161. https://doi.org/https://doi.org/10.2174/1877946808666180914164134
- Shi, J., Deng, Q., Li, Y., Zheng, M., Chai, Z., Wan, C., Zheng, Z., Li, L., Huang, F., & Tang, B. (2018). A rapid and ultrasensitive tetraphenylethylene-based probe with aggregation-induced emission for direct detection of α-amylase in human body fluids. Analytical Chemistry, 90(22), 13775–13782. https://doi.org/https://doi.org/10.1021/acs.analchem.8b04244
- Souza, P. M. D., & Magalhães, P. de O. e. (2010). Application of microbial α-amylase in industry – A review. Brazilian Journal of Microbiology, 41(4), 850–861. https://doi.org/https://doi.org/10.1590/S1517-83822010000400004
- Svensson, B. (1994). Protein engineering in the alpha-amylase family: Catalytic mechanism, substrate specificity, and stability. Plant Molecular Biology, 25(2), 141–157. https://doi.org/https://doi.org/10.1007/BF00023233
- Taha, M., Irshad, M., Imran, S., Rahim, F., Selvaraj, M., Almandil, N. B., Mosaddik, A., Chigurupati, S., Nawaz, F., Ismail, N. H., & Ibrahim, M. (2019). Thiazole based carbohydrazide derivatives as α-amylase inhibitor and their molecular docking study. Heteroatom Chemistry, 2019, 1–8. https://doi.org/https://doi.org/10.1155/2019/7502347
- Taha, M., Ismail, N. H., Lalani, S., Fatmi, M. Q., Atia Tul, W., Siddiqui, S., Khan, K. M., Imran, S., & Choudhary, M. I. (2015). Synthesis of novel inhibitors of α-glucosidase based on the benzothiazole skeleton containing benzohydrazide moiety and their molecular docking studies. European Journal of Medicinal Chemistry, 92, 387–400. https://doi.org/https://doi.org/10.1016/j.ejmech.2015.01.009
- Taha, M., Shah, S. A. A., Imran, S., Afifi, M., Chigurupati, S., Selvaraj, M., Rahim, F., Ullah, H., Zaman, K., & Vijayabalan, S. (2017). Synthesis and in vitro study of benzofuran hydrazone derivatives as novel alpha-amylase inhibitor. Bioorganic Chemistry, 75, 78–85. https://doi.org/https://doi.org/10.1016/j.bioorg.2017.09.002
- Thota, S., Rodrigues, D. A., Pinheiro, P. D. S. M., Lima, L. M., Fraga, C. A., & Barreiro, E. J. (2018). N-acylhydrazones as drugs. Bioorganic & Medicinal Chemistry Letters, 28(17), 2797–2806. https://doi.org/https://doi.org/10.1016/j.bmcl.2018.07.015
- Toropova, A. P., & Toropov, A. A. (2017). The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? The Science of the Total Environment, 586, 466–472. https://doi.org/https://doi.org/10.1016/j.scitotenv.2017.01.198
- Toropova, A. P., & Toropov, A. A. (2019a). QSPR and nano-QSPR: What is the difference? Journal of Molecular Structure, 1182, 141–149. https://doi.org/https://doi.org/10.1016/j.molstruc.2019.01.040
- Toropova, A. P., & Toropov, A. A. (2019b). Does the index of ideality of correlation detect the better model correctly? Molecular Informatics, 38(8–9), 1800157. https://doi.org/https://doi.org/10.1002/minf.201800157
- Toropova, A. P., & Toropov, A. A. (2019c). The index of ideality of correlation: Improvement of models for toxicity to algae. Natural Product Research, 33(15), 2200–2207. https://doi.org/https://doi.org/10.1080/14786419.2018.1493591
- Toropova, A. P., Toropov, A. A., Beeg, M., Gobbi, M., & Salmona, M. (2017). Utilization of the Monte Carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides. Current Drug Discovery Technologies, 14(4), 229–243. https://doi.org/https://doi.org/10.2174/1570163814666170525114128
- Toropova, A. P., Toropov, A. A., Benfenati, E., Leszczynska, D., & Leszczynski, J. (2015). QSAR model as a random event: A case of rat toxicity. Bioorganic & Medicinal Chemistry, 23(6), 1223–1230. https://doi.org/https://doi.org/10.1016/j.bmc.2015.01.055
- Toropova, A. P., Toropov, A. A., Veselinovic, A. M., Veselinovic, J. B., Leszczynska, D., & Leszczynski, J. (2019). Semi-correlations combined with the index of ideality of correlation: A tool to build up model of mutagenic potential. Molecular and Cellular Biochemistry, 452(1–2), 133–140. https://doi.org/https://doi.org/10.1007/s11010-018-3419-4
- Toropov, A. A., & Toropova, A. P. (2018). Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation. Anticancer Research, 38(11), 6189–6194. https://doi.org/https://doi.org/10.21873/anticanres.12972
- Toropov, A. A., & Toropova, A. P. (2019). Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicology Mechanisms and Methods, 29(1), 43–52. https://doi.org/https://doi.org/10.1080/15376516.2018.1506851
- Toropov, A. A., Toropova, A. P., & Benfenati, E. (2019). The index of ideality of correlation: QSAR model of acute toxicity for Zebrafish (Danio rerio) embryo. International Journal of Environmental Research, 13(2), 387–394. https://doi.org/https://doi.org/10.1007/s41742-019-00183-y
- Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/https://doi.org/10.1002/jcc.21334
- Version, M., & 6.2.2. (2014). calculation module developed by ChemAxon. http://www.chemaxon.com/products/marvin/marvinsketch/
- Veselinovic, A. M., Milosavljevic, J. B., Toropov, A. A., & Nikolic, G. M. (2013). SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT(1A) receptor ligands using CORAL . European Journal of Pharmaceutical Sciences: Official Journal of the European Federation for Pharmaceutical Sciences, 48(3), 532–541. https://doi.org/https://doi.org/10.1016/j.ejps.2012.12.021
- Veselinovic, J. B., Nikolic, G. M., Trutic, N. V., Zivkovic, J. V., & Veselinovic, A. M. (2015). Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase. SAR and QSAR in Environmental Research, 26(6), 449–460. https://doi.org/https://doi.org/10.1080/1062936x.2015.1049665
- Wang, J., Wang, W., Kollman, P. A., & Case, D. A. (2006). Automatic atom type and bond type perception in molecular mechanical calculations. Journal of Molecular Graphics & Modelling, 25(2), 247–260. https://doi.org/https://doi.org/10.1016/j.jmgm.2005.12.005