References
- O. Erdmann, Untersuchungen über den indigo, J. Prakti. Chemie 19 (1840), pp. 321–362. doi:https://doi.org/10.1002/prac.18400190161.
- A. Laurent, Recherches sur l’indigo, Ann. Chim. Phys. 3 (1840), pp. 393–434.
- W. Sumpter, Inhibitors of 17 beta-hydroxysteroid dehydrogenases, Chem. Rev. 34 (1954), pp. 407.
- J.F.M. Da Silva, S.J. Garden, and A.C. Pinto, The chemistry of isatins: A review from 1975 to 1999, J. Braz. Chem. Soc. 12 (2001), pp. 273–324. doi:https://doi.org/10.1590/S0103-50532001000300002.
- B.V. Silva, Isatin, a versatile molecule: Studies in brazil, J. Braz. Chem. Soc. 24 (2013), pp. 707–720.
- G. Zapata-Sudo, L.B. Pontes, D. Gabriel, T.C. Mendes, N.M. Ribeiro, A.C. Pinto, M.M. Trachez, and R.T. Sudo, Sedative-hypnotic profile of novel isatin ketals, Pharmacol. Biochem. Behav. 86 (2007), pp. 678–685. doi:https://doi.org/10.1016/j.pbb.2007.02.013.
- S.N. Pandeya, D. Sriram, G. Nath, and E. De Clercq, Synthesis and antimicrobial activity of Schiff and mannich bases of isatin and its derivatives with pyrimidine, Farmaco 54 (1999), pp. 624–628. doi:https://doi.org/10.1016/S0014-827X(99)00075-0.
- S.K. Sridhar, M. Saravanan, and A. Ramesh, Synthesis and antibacterial screening of hydrazones, Schiff, and mannich bases of isatin derivatives, Eur. J. Med. Chem. 36 (2001), pp. 615–625. doi:https://doi.org/10.1016/S0223-5234(01)01255-7.
- S. Sun and J.H. Schiller, Angiogenesis inhibitors in the treatment of lung cancer, Crit. Rev. Oncol. Hematol. 62 (2007), pp. 93–104. doi:https://doi.org/10.1016/j.critrevonc.2007.01.002.
- K.L. Vine, L. Matesic, J.M. Locke, M. Ranson, and D. Skropeta, Cytotoxic and anticancer activities of isatin and its derivatives: A comprehensive review from 2000-2008, Anticancer Agents Med. Chem. 9 (2009), pp. 397–414. doi:https://doi.org/10.2174/1871520610909040397.
- P. Singh, P. Sharma, A. Anand, P.M. Bedi, T. Kaur, A.K. Saxena, and V. Kumar, Azidealkyne cycloaddition en route to novel 1h-1,2,3-triazole tethered isatin conjugates with in vitro cytotoxic evaluation, Eur. J. Med. Chem. 55 (2012), pp. 455–461. doi:https://doi.org/10.1016/j.ejmech.2012.06.057.
- J. Robert and C. Jarry, Multidrug resistance reversal agents, J. Med. Chem. 46 (2003), pp. 4805–4817. doi:https://doi.org/10.1021/jm030183a.
- S. Hassan, S. Dhar, M. Sandstrom, D. Arsenau, M. Budnikova, I. Lokot, N. Lobanov, M.O. Karlsson, R. Larsson, and E. Lindhagen, Cytotoxic activity of a new paclitaxel formulation, pacliex, in vitro and in vivo, Cancer Chemother. Pharmacol. 55 (2005), pp. 47–54.
- S. Jin, M. Li, C. Zhu, V. Tran, and B. Wang, Computer-based de novo design, synthesis, and evaluation of boronic acid-based artificial receptors for selective recognition of dopamine, Chembiochem 9 (2008), pp. 1431–1438. doi:https://doi.org/10.1002/cbic.200700663.
- B. Elidrissi, A. Ousaa, A. Aouidate, H. Zaki, M. Ajana, T. Lakhlifi, and M. Bouachrine, 3D-QSAR studies of isatin derivatives with anti-cancer in vitro: Advanced CoMFA, CoMSIA and docking methods, Chem. Sci. J. 8 (2017), pp. 2–8.
- E. Pourbasheer and M. Amanlou, 3D-QSAR analysis of anti-cancer agents by CoMFA, CoMSIA, Med. Chem. Res. 23 (2014), pp. 800–809. doi:https://doi.org/10.1007/s00044-013-0676-3.
- V. Pawar, D. Lokwani, S. Bhandari, D. Mitra, S. Sabde, K. Bothara, and A. Madgulkar, Design of potential reverse transcriptase inhibitor containing isatin nucleus using molecular modeling studies, Bioorg. Med. Chem. 18 (2010), pp. 3198–3211. doi:https://doi.org/10.1016/j.bmc.2010.03.030.
- R.K. Prasad, T. Narsinghani, and R. Sharma, QSAR analysis of novel n-alkyl substituted isatins derivatives as anticancer agents, J. Chem. Pharm. Res. 1 (2009), pp. 199–206.
- Q. Wang, R.H. Mach, and D.E. Reichert, Docking and 3D-QSAR studies on isatin sulfonamide analogues as caspase-3 inhibitors, J. Chem. Inf. Model. 49 (2009), pp. 1963–1973. doi:https://doi.org/10.1021/ci900144x.
- A.J. Hopfinger, S. Wang, J.S. Tokarski, B. Jin, M. Albuquerque, P.J. Madhav, and C. Duraiswami, Construction of 3D-QSAR models using the 4D-QSAR analysis formalism, J. Am. Chem. Soc. 119 (1997), pp. 10509–10524. doi:https://doi.org/10.1021/ja9718937.
- J.S. Duca and A.J. Hopfinger, Estimation of molecular similarity based on 4D-QSAR analysis: Formalism and validation, J. Chem. Inf. Comp. Sci. 41 (2001), pp. 1367–1387. doi:https://doi.org/10.1021/ci0100090.
- C.H. Andrade, K.F. Pasqualoto, E.I. Ferreira, and A.J. Hopfinger, 4D-QSAR: Perspectives in drug design, Molecules 15 (2010), pp. 3281–3294. doi:https://doi.org/10.3390/molecules15053281.
- O.M. Becker, Y. Levy, and O. Ravitz, Flexibility, conformation spaces, and bioactivity, J. Phys. Chem. B 104 (2000), pp. 2123–2135. doi:https://doi.org/10.1021/jp992268m.
- O.F. Guner, History and evolution of the pharmacophore concept in computer-aided drug design, Curr. Top. Med. Chem. 2 (2002), pp. 1321–1332. doi:https://doi.org/10.2174/1568026023392940.
- A. Dimoglo, P. Vlad, N. Shvets, M. Coltsa, Y. Guzel, M. Saracoglu, E. Saripinar, and S. Patat, Electronic-topolocigal investigations of the relationship between chemical structure and ambergris odor, New J. Chem. 19 (1995), pp. 1217–1226.
- I.B. Bersuker, S. Bahceci, J.E. Boggs, and R.S. Pearlman, A novel electron conformational approach to molecular modeling for QSAR by identification of pharmacophore and anti-pharmacophore shielding, SAR QSAR Environ. Res. 10 (1999), pp. 157–173. doi:https://doi.org/10.1080/10629369908039174.
- I.B. Bersuker, QSAR without arbitrary descriptors: The electron-conformational method, J. Comput. Aided Mol. Des. 22 (2008), pp. 423–430. doi:https://doi.org/10.1007/s10822-008-9191-x.
- K.K. Bedia, O. Elçin, U. Seda, K. Fatma, S. Nathaly, R. Sevim, and A. Dimoglo, Synthesis and characterization of novel hydrazide–hydrazones and the study of their structure–antituberculosis activity, Eur. J. Med. Chem. 41 (2006), pp. 1253–1261. doi:https://doi.org/10.1016/j.ejmech.2006.06.009.
- W. Karcher and J. Devillers, Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1990.
- J. Devillers and A.T. Balaban, Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach Science Publishers, The Netherlands, 1999.
- J. Devillers and W. Karcher, Applied Multivariate Analysis in SAR and Environmental Studies, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991.
- I.B. Bersuker, Pharmacophore identification and quantitative bioactivity prediction using the electron-conformational method, Curr. Pharm. Des. 9 (2003), pp. 1575–1606. doi:https://doi.org/10.2174/1381612033454586.
- E. Saripinar, N. Gecen, K. Sahin, and E. Yanmaz, Pharmacophore identification and bioactivity prediction for triaminotriazine derivatives by electron conformational-genetic algorithm QSAR method, Eur. J. Med. Chem. 45 (2010), pp. 4157–4168. doi:https://doi.org/10.1016/j.ejmech.2010.06.007.
- N. Gecen, E. Saripinar, E. Yanmaz, and K. Sahin, Application of electron conformational genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: Pharmacophore identification and bioactivity prediction, J. Mol. Model. 18 (2012), pp. 65–82. doi:https://doi.org/10.1007/s00894-011-1024-5.
- K. Sahin, E. Saripinar, E. Yanmaz, and N. Geçen, Quantitative bioactivity prediction and pharmacophore identification for benzotriazine derivatives using the electron conformational–genetic algorithm in QSAR, SAR QSAR Environ. Res. 22 (2011), pp. 217–238. doi:https://doi.org/10.1080/1062936X.2010.548341.
- E. Yanmaz, E. Saripinar, K. Sahin, N. Gecen, and F. Copur, 4D-QSAR analysis and pharmacophore modeling: Electron conformational-genetic algorithm approach for penicillins, Bioorg. Med. Chem. 19 (2011), pp. 2199–2210. doi:https://doi.org/10.1016/j.bmc.2011.02.035.
- B. Tuzun, S.C. Yavuz, N. Sabanci, and E. Saripinar, 4D-QSAR study of some pyrazole pyridine carboxylic acid derivatives by electron conformational-genetic algorithm method, Curr. Comput. Aided Drug Des. 14 (2018), pp. 370–384. doi:https://doi.org/10.2174/1573409914666180514094202.
- S. Yavuz, N. Sabanci, and E. Saripinar, Pharmacophore modelling and 4D-QSAR study of ruthenium (ii) arene complexes as anticancer agents (inhibitors) by electron conformational-genetic algorithm method, Curr. Comput. Aided Drug Des. 14 (2018), pp. 79–94. doi:https://doi.org/10.2174/1573409913666170529103206.
- Z. Huang, Bcl-2 family proteins as targets for anticancer drug design, Oncogene 19 (2000), pp. 6627–6631. doi:https://doi.org/10.1038/sj.onc.1204087.
- R. Kim, K. Tanabe, Y. Uchida, M. Emi, H. Inoue, and T. Toge, Current status of the molecular mechanisms of anticancer drug-induced apoptosis. The contribution of molecular-level analysis to cancer chemotherapy, Cancer Chemother. Pharmacol. 50 (2002), pp. 343–352. doi:https://doi.org/10.1007/s00280-002-0522-7.
- M. Hanada, C. Aime-Sempe, T. Sato, and J.C. Reed, Structure-function analysis of bcl- 2 protein. Identification of conserved domains important for homodimerization with bcl-2 and heterodimerization with bax, J. Biol. Chem. 270 (1995), pp. 11962–11969. doi:https://doi.org/10.1074/jbc.270.20.11962.
- O.I. Olopade, M.O. Adeyanju, A.R. Safa, F. Hagos, R. Mick, C.B. Thompson, and W.M. Recant, Overexpression of bcl-x protein in primary breast cancer is associated with high tumor grade and nodal metastases, Cancer J. Sci. Am. 3 (1997), pp. 230–237.
- A. Cane, M.C. Tournaire, D. Barritault, and M. Crumeyrolle-Arias, The endogenous oxindoles 5-hydroxyoxindole and isatin are antiproliferative and proapoptotic, Biochem. Biophys. Res. Commun. 276 (2000), pp. 379–384. doi:https://doi.org/10.1006/bbrc.2000.3477.
- N. Igosheva, C. Lorz, E. O’conner, V. Glover, and H. Mehmet, Isatin, an endogenous monoamine oxidase inhibitor, triggers a dose- and time-dependent switch from apoptosis to necrosis in human neuroblastoma cells, Neurochem. Int. 47 (2005), pp. 216–224. doi:https://doi.org/10.1016/j.neuint.2005.02.011.
- J. Song, L. Hou, C. Ju, J. Zhang, Y. Ge, and W. Yue, Isatin inhibits proliferation and induces apoptosis of sh-sy5y neuroblastoma cells in vitro and in vivo, Eur. J. Pharmacol. 702 (2013), pp. 235–241. doi:https://doi.org/10.1016/j.ejphar.2013.01.017.
- T.E. Wang, Y.K. Wang, J. Jin, B.L. Xu, and X.G. Chen, A novel derivative of quinazoline, wyk431 induces g2/m phase arrest and apoptosis in human gastric cancer bgc823 cells through the pi3k/akt pathway, Int. J. Oncol. 45 (2014), pp. 771–781. doi:https://doi.org/10.3892/ijo.2014.2458.
- R.E. Ferraz De Paiva, E.G. Vieira, D. Rodrigues Da Silva, C.A. Wegermann, and A.M. Costa Ferreira, Anticancer compounds based on isatin-derivatives: Strategies to ameliorate selectivity and efficiency, Front. Mol. Biosci. 7 (2020), pp. 627272. doi:https://doi.org/10.3389/fmolb.2020.627272.
- K.L. Vine, J.M. Locke, M. Ranson, K. Benkendorff, S.G. Pyne, and J.B. Bremner, In vitro cytotoxicity evaluation of some substituted isatin derivatives, Bioorg. Med. Chem. 15 (2007), pp. 931–938.
- K.L. Vine, J.M. Locke, M. Ranson, S.G. Pyne, and J.B. Bremner, An investigation into the cytotoxicity and mode of action of some novel n-alkyl-substituted isatins, J. Med. Chem. 50 (2007), pp. 5109–5117. doi:https://doi.org/10.1021/jm0704189.
- L. Matesic, J.M. Locke, J.B. Bremner, S.G. Pyne, D. Skropeta, M. Ranson, and K.L. Vine, N-Phenethyl and N-Naphthylmethyl isatins and analogues as in vitro cytotoxic agents, Bioorg. Med. Chem. 16 (2008), pp. 3118–3124. doi:https://doi.org/10.1016/j.bmc.2007.12.026.
- L. Hou, C. Ju, J. Zhang, J. Song, Y. Ge, and W. Yue, Antitumor effects of isatin on human neuroblastoma cell line (sh-sy5y) and the related mechanism, Eur. J. Pharmacol. 589 (2008), pp. 27–31. doi:https://doi.org/10.1016/j.ejphar.2008.04.061.
- A. Nagarsenkar, L. Guntuku, S.D. Guggilapu, D.B.K.S. Gannoju, V.G.M. Naidu, and N.B. Bathini, Synthesis and apoptosis inducing studies of triazole linked 3-benzylidene isatin derivatives, Eur. J. Med. Chem. 124 (2016), pp. 782–793. doi:https://doi.org/10.1016/j.ejmech.2016.09.009.
- J.W. Park, Y.J. Choi, S. Suh, W.K. Baek, M.H. Suh, I.N. Jin, D.S. Min, J.H. Woo, J.S. Chang, A. Passaniti, Y.H. Lee, and T.K. Kwon, Bcl-2 overexpression attenuates resveratrol induced apoptosis in u937 cells by inhibition of caspase-3 activity, Carcinogenesis 22 (2001), pp. 1633–1639. doi:https://doi.org/10.1093/carcin/22.10.1633.
- R. Sabet, M. Mohammadpour, A. Sadeghi, and A. Fassihi, QSAR study of isatin analogues as in vitro anti-cancer agents, Eur. J. Med. Chem. 45 (2010), pp. 1113–1118. doi:https://doi.org/10.1016/j.ejmech.2009.12.010.
- Spartan, V.06, Wavefunction, Inc., Irvine, CA, 2006.
- W.J. Hehre, A Guide to Molecular Mechanics and Quantum Chemical Calculations, Wavefunction, Inc., Irvine, CA, 2003.
- V. Librando and A. Alparone, The role of electronic properties to the mutagenic activity of 1,6- and 3,6-dinitrobenzo[a]pyrene isomers, J. Hazard Mater. 161 (2009), pp. 1338–1346. doi:https://doi.org/10.1016/j.jhazmat.2008.04.095.
- I.B. Bersuker, S. Bahceci, J.E. Boggs, and R.S. Pearlman, An electron-conformational method of identification of pharmacophore and anti-pharmacophore shielding: Application to rice blast activity, J. Comput. Aided Mol. Des. 13 (1999), pp. 419–434. doi:https://doi.org/10.1023/A:1008052914704.
- E. Saripinar, Y. Guzel, S. Patat, I. Yildirim, Y. Akcamur, and A.S. Dimoglo, Electron topological investigation of structure-antitubercular activity relationship of thiosemicarbazone derivatives, Arzneimittelforschung 46 (1996), pp. 824–828.
- A.V. Marenich, P.H. Yong, I.B. Bersuker, and J.E. Boggs, Quantitative antidiabetic activity prediction for the class of guanidino- and aminoguanidinopropionic acid analogs based on electron conformational studies, J. Chem. Inf. Model. 48 (2008), pp. 556–568. doi:https://doi.org/10.1021/ci700401p.
- M. Sakai, K. Nagayasu, N. Shibui, C. Andoh, K. Takayama, H. Shirakawa, and S. Kaneko, Prediction of pharmacological activities from chemical structures with graph convolutional neural networks, Sci. Rep. 11 (2021), pp. 525. doi:https://doi.org/10.1038/s41598-020-80113-7.
- A. Altun, K. Golcuk, M. Kumru, and A.F. Jalbout, Electron-conformational study for the structure-hallucinogenic activity relationships of phenylalkylamines, Bioorg. Med. Chem. 11 (2003), pp. 3861–3868. doi:https://doi.org/10.1016/S0968-0896(03)00437-1.
- T. Pavlov, M. Todorov, G. Stoyanova, P. Schmieder, H. Aladjov, R. Serafimova, and O. Mekenyan, Conformational coverage by a genetic algorithm: Saturation of conformational space, J. Chem. Inf. Model. 47 (2007), pp. 851–863. doi:https://doi.org/10.1021/ci700014h.
- Matlab and Statistics Toolbox Release 2012b, The Mathworks, Inc., Natick, MA, 2012.
- B. Zhang, D. Kilburg, P. Eastman, V.S. Pande, and E. Gallicchio, Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units, J. Comput. Chem. 38 (2017), pp. 740–752. doi:https://doi.org/10.1002/jcc.24745.
- H. Goldstein, C. Poole Jr, and J.L. Safko, Classical Mechanics, 3rd ed., Pearson, Stockton, California, 2001.
- T.R. Stouch and P.C. Jurs, A simple method for the representation, quantification, and comparison of the volumes and shapes of chemical compounds, J. Chem. Inf. Comp. Sci. 26 (1986), pp. 4–12. doi:https://doi.org/10.1021/ci00049a002.
- R.H. Rohrbaugh and P.C. Jurs, Molecular shape and the prediction of high-performance liquid chromatographic retention indexes of polycyclic aromatic hydrocarbons, Anal. Chem. 59 (1987), pp. 1048–1054. doi:https://doi.org/10.1021/ac00134a025.
- R. Parthasarathi, M. Elango, P. Jaganathan, V. Subramanian, D. Roy, U. Sarkar, and P. Chattaraj, Application of quantum chemical descriptors in computational medicinal chemistry and chemoinformatics, Ind. J. Chem. – Inorg. Phy. Theor. Anal. Chem. 45 (2006), pp. 111–125.
- V. Consonni, D. Ballabio, and R. Todeschini, Comments on the definition of the q2 parameter for QSAR validation, J. Chem. Inf. Model. 49 (2009), pp. 1669–1678. doi:https://doi.org/10.1021/ci900115y.
- Sybyl Theory Manual, Version 6.7, Tripos Associates Inc., St. Louis, MO, 2000.
- A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
- P. Gramatica, P. Pilutti, and E. Papa, Validated QSAR prediction of oh tropospheric degradation of vocs: Splitting into training-test sets and consensus modeling, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1794–1802. doi:https://doi.org/10.1021/ci049923u.
- S. Van Damme and P. Bultinck, A new computer program for QSAR-analysis: Arte-QSAR, J. Comput. Chem. 28 (2007), pp. 1924–1928. doi:https://doi.org/10.1002/jcc.20664.
- G. Schuurmann, R.U. Ebert, J. Chen, B. Wang, and R. Kuhne, External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean, J. Chem. Inf. Model. 48 (2008), pp. 2140–2145. doi:https://doi.org/10.1021/ci800253u.
- L.I. Lin, Assay validation using the concordance correlation coefficient, Biometrics 48 (1992), pp. 599–604. doi:https://doi.org/10.2307/2532314.
- J.L. Banks, H.S. Beard, Y. Cao, A.E. Cho, W. Damm, R. Farid, A.K. Felts, T.A. Halgren, D.T. Mainz, J.R. Maple, R. Murphy, D.M. Philipp, M.P. Repasky, L.Y. Zhang, B.J. Berne, R.A. Friesner, E. Gallicchio, and R.M. Levy, Integrated modeling program, applied chemical theory (impact), J. Comput. Chem. 26 (2005), pp. 1752–1780. doi:https://doi.org/10.1002/jcc.20292.
- K.K. Kakarala, K. Jamil, and V. Devaraji, Structure and putative signaling mechanism of protease activated receptor 2 (par2) - a promising target for breast cancer, J. Mol. Graph. Model. 53 (2014), pp. 179–199. doi:https://doi.org/10.1016/j.jmgm.2014.07.012.
- J.C. Shelley, A. Cholleti, L.L. Frye, J.R. Greenwood, M.R. Timlin, and M. Uchimaya, Epik: A software program for pk(a) prediction and protonation state generation for drug-like molecules, J. Comput. Aided Mol. Des. 21 (2007), pp. 681–691. doi:https://doi.org/10.1007/s10822-007-9133-z.
- A.J. Souers, J.D. Leverson, E.R. Boghaert, S.L. Ackler, N.D. Catron, J. Chen, B.D. Dayton, H. Ding, S.H. Enschede, W.J. Fairbrother, D.C.S. Huang, S.G. Hymowitz, S. Jin, S.L. Khaw, P.J. Kovar, L.T. Lam, J. Lee, H.L. Maecker, K.C. Marsh, K.D. Mason, M.J. Mitten, P.M. Nimmer, A. Oleksijew, C.H. Park, C.-M. Park, D.C. Phillips, A.W. Roberts, D. Sampath, J.F. Seymour, M.L. Smith, G.M. Sullivan, S.K. Tahir, C. Tse, M.D. Wendt, Y. Xiao, J.C. Xue, H. Zhang, R.A. Humerickhouse, S.H. Rosenberg, and S.W. Elmore, Abt-199, A potent and selective bcl-2 inhibitor, achieves antitumor activity while sparing platelets, Nat. Med. 19 (2013), pp. 202–208. doi:https://doi.org/10.1038/nm.3048.
- S.K. Tripathi, R. Muttineni, and S.K. Singh, Extra precision docking, free energy calculation and molecular dynamics simulation studies of cdk2 inhibitors, J. Theor. Biol. 334 (2013), pp. 87–100. doi:https://doi.org/10.1016/j.jtbi.2013.05.014.
- G.M. Sastry, M. Adzhigirey, T. Day, R. Annabhimoju, and W. Sherman, Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments, J. Comput. Aid. Mol. Des. 27 (2013), pp. 221–234.
- R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky, E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, and P.S. Shenkin, Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, J. Med. Chem. 47 (2004), pp. 1739–1749. doi:https://doi.org/10.1021/jm0306430.
- K. Raha and K.M. Merz, A quantum mechanics-based scoring function: Study of zinc ion-mediated ligand binding, J. Am. Chem. Soc. 126 (2004), pp. 1020–1021. doi:https://doi.org/10.1021/ja038496i.
- T.A. Binkowski, W. Jiang, B. Roux, W.F. Anderson, and A. Joachimiak, Virtual highthroughput ligand screening, Methods Mol. Biol. 1140 (2014), pp. 251–261.
- X.Y. Meng, H.X. Zhang, M. Mezei, and M. Cui, Molecular docking: A powerful approach for structure-based drug discovery, Curr. Comput. Aided Drug Des. 7 (2011), pp. 146–157. doi:https://doi.org/10.2174/157340911795677602.
- S. Subhani, A. Jayaraman, and K. Jamil, Homology modelling and molecular docking of mdr1 with chemotherapeutic agents in non-small cell lung cancer, Biomed. Pharmacother. 71 (2015), pp. 37–45. doi:https://doi.org/10.1016/j.biopha.2015.02.009.
- S. Roy, A. Kumar, M.H. Baig, M. Masajik, and I. Provaznik, Virtual screening, admet profiling, molecular docking and dynamics approaches to search for potent selective natural molecules-based inhibitors against metallothionein-iii to study alzheimer’s disease, Methods 83 (2015), pp. 105–110. doi:https://doi.org/10.1016/j.ymeth.2015.04.021.
- T. Hou, J. Wang, Y. Li, and W. Wang, Assessing the performance of the molecular mechanics/poisson Boltzmann surface area and molecular mechanics/generalized born surface area methods. The accuracy of ranking poses generated from docking, J. Comput. Chem. 32 (2011), pp. 866–877. doi:https://doi.org/10.1002/jcc.21666.
- K.J. Bowers, E. Chow, H. Xu, R.O. Dror, M.P. Eastwood, B.A. Gregersen, J.L. Klepeis, I. Kolossvary, M.A. Moraes, F.D. Sacerdoti, J.K. Salmon, Y. Shan, and D.E. Shaw, Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters, Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 2006.
- H.J.C. Berendsen, J.P.M. Postma, W.F. Van Gunsteren, and J. Hermans, Interaction Models for Water in Relation to Protein Hydration, Springer Netherlands, Dordrecht, 1981, pp. 331–342.
- R.A. Friesner, R.B. Murphy, M.P. Repasky, L.L. Frye, J.R. Greenwood, T.A. Halgren, P.C. Sanschagrin, and D.T. Mainz, Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes, J. Med. Chem. 49 (2006), pp. 6177–6196. doi:https://doi.org/10.1021/jm051256o.
- W.G. Hoover, Canonical dynamics: Equilibrium phase-space distributions, Phys. Rev. Gen. Phys. 31 (1985), pp. 1695–1697. doi:https://doi.org/10.1103/PhysRevA.31.1695.
- Z. Ma and M. Tuckerman, Constant pressure ab initio molecular dynamics with discrete variable representation basis sets, J. Chem. Phys. 133 (2010), p. 184110. doi:https://doi.org/10.1063/1.3499812.
- M.P. Jacobson, D.L. Pincus, C.S. Rapp, T.J. Day, B. Honig, D.E. Shaw, and R.A. Friesner, A hierarchical approach to all-atom protein loop prediction, Proteins 55 (2004), pp. 351–367. doi:https://doi.org/10.1002/prot.10613.
- Delphi 2009 20 VER200, 2009; software available at http://www.Borland.Com/Delphi.
- A. Dimoglo, N. Shvets, I. Tetko, and D. Livingstone, Electronic-topological investigation of the structure – Acetylcholinesterase inhibitor activity relationship in the series of n-benzylpiperidine derivatives, Mol. Inform. 20 (2001), pp. 31–45.
- J.G. Topliss and R.P. Edwards, Chance factors in studies of quantitative structure-activity relationships, J. Med. Chem. 22 (1979), pp. 1238–1244. doi:https://doi.org/10.1021/jm00196a017.
- R.D. Cramer III, J.D. Bunce, D.E. Patterson, and I.E. Frank, Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies, Mol. Inform. 7 (1988), pp. 18–25.
- S. Wold, Cross-validatory estimation of the number of components in factor and principal components models, Technometrics 20 (1978), pp. 397–405. doi:https://doi.org/10.1080/00401706.1978.10489693.