[1]
Kinzler, K.W.; Vogelstein, B. Cancer-susceptibility genes. Gatekeepers and caretakers. Nature, 1997, 386(6627), 761-763.
[2]
Suzuki, T.; Miyata, N. Non-hydroxamate histone deacetylase inhibitors. Curr. Med. Chem., 2005, 12(24), 2867-2880.
[3]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer satistics, 2017. CA Cancer J. Clin., 2017, 67(1), 7-30.
[4]
Strahl, B.D.; Allis, C.D. The language of covalent histone modifications. Nature, 2000, 403(6765), 41-45.
[5]
Rajak, H.; Singh, A.; Raghuwanshi, K.; Kumar, R.; Dewangan, P.K.; Veerasamy, R.; Sharma, P.C.; Dixit, A.; Mishra, P. A structural insight into hydroxamic acid based histone deacetylase inhibitors for the presence of anticancer activity. Curr. Med. Chem., 2014, 21(23), 2642-2664.
[6]
Singh, A.; Patel, P.; Patel, V.K.; Jain, D.K.; Veerasamy, R.; Sharma, P.C.; Rajak, H. Histone deacetylase inhibitors for the treatment of colorectal cancer: Recent progress and future prospects. Curr. Cancer Drug Targets, 2017, 17(5), 456-466.
[7]
Roth, S.Y.; Denu, J.M.; Allis, C.D. Histone acetyltransferases. Annu. Rev. Biochem., 2001, 70, 81-120.
[8]
Thiagalingam, S.; Cheng, K.H.; Lee, H.J.; Mineva, N.; Thiagalingam, A.; Ponte, J.F. Histone deacetylases: Unique players in shaping the epigenetic histone code. Ann. N. Y. Acad. Sci., 2003, 983, 84-100.
[9]
Stimson, L.; La Thangue, N.B. Biomarkers for predicting clinical responses to HDAC inhibitors. Cancer Lett., 2009, 280(2), 177-183.
[10]
Gray, S.G.; Ekstrom, T.J. The human histone deacetylase family. Exp. Cell Res., 2001, 262(2), 75-83.
[11]
Minucci, S.; Pelicci, P.G. Histone deacetylase inhibitors and the promise of epigenetic (and more) treatments for cancer. Nat. Rev. Cancer, 2006, 6(1), 38-51.
[12]
Brunmeir, R.; Lagger, S.; Seiser, C. Histone deacetylase HDAC1/HDAC2-controlled embryonic development and cell differentiation. Int. J. Dev. Biol., 2009, 53(2-3), 275-289.
[13]
Gallinari, P.; Di Marco, S.; Jones, P.; Pallaoro, M.; Steinkuhler, C. HDACs, histone deacetylation and gene transcription: From molecular biology to cancer therapeutics. Cell Res., 2007, 17(3), 195-211.
[14]
Spiegel, S.; Milstien, S.; Grant, S. Endogenous modulators and pharmacological inhibitors of histone deacetylases in cancer therapy. Oncogene, 2012, 31(5), 537-551.
[15]
Frikeche, J.; Peric, Z.; Brissot, E.; Gregoire, M.; Gaugler, B.; Mohty, M. Impact of HDAC inhibitors on dendritic cell functions. Exp. Hematol., 2012, 40(10), 783-791.
[16]
Muller, B.M.; Jana, L.; Kasajima, A.; Lehmann, A.; Prinzler, J.; Budczies, J.; Winzer, K.J.; Dietel, M.; Weichert, W.; Denkert, C. Differential expression of histone deacetylases HDAC1, 2 and 3 in human breast cancer overexpression of HDAC2 and HDAC3 is associated with clinicopathological indicators of disease progression. BMC Cancer, 2013, 13, 215.
[17]
Barneda-Zahonero, B.; Parra, M. Histone deacetylases and cancer. Mol. Oncol., 2012, 6(6), 579-589.
[18]
Dokmanovic, M.; Clarke, C.; Marks, P.A. Histone deacetylase inhibitors: Overview and perspectives. Mol. Cancer Res., 2007, 5(10), 981-989.
[19]
Kozikowski, A.P.; Chen, Y.; Gaysin, A.; Chen, B.; D’Annibale, M.A.; Suto, C.M.; Langley, B.C. Functional differences in epigenetic modulators-superiority of mercaptoacetamide-based histone deacetylase inhibitors relative to hydroxamates in cortical neuron neuroprotection studies. J. Med. Chem., 2007, 50(13), 3054-3061.
[20]
Schafer, S.; Saunders, L.; Eliseeva, E.; Velena, A.; Jung, M.; Schwienhorst, A.; Strasser, A.; Dickmanns, A.; Ficner, R.; Schlimme, S.; Sippl, W.; Verdin, E.; Jung, M. Phenylalanine-containing hydroxamic acids as selective inhibitors of class IIb histone deacetylases (HDACs). Bioorg. Med. Chem., 2008, 16(4), 2011-2033.
[21]
Gryder, B.E.; Sodji, Q.H.; Oyelere, A.K. Targeted cancer therapy: Giving histone deacetylase inhibitors all they need to succeed. Future Med. Chem., 2012, 4(4), 505-524.
[22]
Lindsley, C.W. Novel drug approvals in 2015 and thus far in 2016. ACS Chem. Neurosci., 2016, 7(9), 1175-1176.
[23]
Mailankody, S.; Prasad, V. Five years of cancer drug approvals: Innovation, efficacy, and costs. JAMA Oncol., 2015, 1(4), 539-540.
[24]
Singh, A.; Patel, V.K.; Jain, D.K.; Patel, P.; Rajak, H. Panobinostat as Pan-deacetylase inhibitor for the treatment of pancreatic cancer: Recent progress and future prospects. Oncol. Ther., 2016, 4(1), 73-89.
[25]
Mottamal, M.; Zheng, S.; Huang, T.L.; Wang, G. Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents. Molecules, 2015, 20(3), 3898-3941.
[26]
Wu, S.; Qi, W.; Su, R.; Li, T.; Lu, D.; He, Z. CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides. Eur. J. Med. Chem., 2014, 84, 100-106.
[27]
Nair, S.B.; Teli, M.K.; Pradeep, H.; Rajanikant, G.K. Computational identification of novel histone deacetylase inhibitors by docking based QSAR. Comput. Biol. Med., 2012, 42(6), 697-705.
[28]
Cheng, J.; Qin, J.; Guo, S.; Qiu, H.; Zhong, Y. Design, synthesis and evaluation of novel HDAC inhibitors as potential antitumor agents. Bioorg. Med. Chem. Lett., 2014, 24(19), 4768-4772.
[30]
Yao, Y.; Liao, C.; Li, Z.; Wang, Z.; Sun, Q.; Liu, C.; Yang, Y.; Tu, Z.; Jiang, S. Design, synthesis, and biological evaluation of 1, 3-disubstituted-pyrazole derivatives as new class I and IIb histone deacetylase inhibitors. Eur. J. Med. Chem., 2014, 86, 639-652.
[31]
Su, H.; Nebbioso, A.; Carafa, V.; Chen, Y.; Yang, B.; Altucci, L.; You, Q. Design, synthesis and biological evaluation of novel compounds with conjugated structure as anti-tumor agents. Bioorg. Med. Chem., 2008, 16(17), 7992-8002.
[32]
Patel, V.K.; Singh, A.; Jain, D.K.; Patel, P.; Veerasamy, R.; Sharma, P.C.; Rajak, H. Combretastatin A-4 based thiophene derivatives as antitumor agent: Development of structure activity correlation model using 3D-QSAR, pharmacophore and docking studies. Future. J. Pharm. Sci., 2017, 3(2), 71-78.
[33]
Jin, Y.; Qi, P.; Wang, Z.; Shen, Q.; Wang, J.; Zhang, W.; Song, H. 3D-QSAR study of combretastatin A-4 analogs based on molecular docking. Molecules, 2011, 16(8), 6684-6700.
[34]
Watts, K.S.; Dalal, P.; Murphy, R.B.; Sherman, W.; Friesner, R.A.; Shelley, J.C. ConfGen: A conformational search method for efficient generation of bioactive conformers. J. Chem. Inf. Model., 2010, 50(4), 534-546.
[36]
Teli, M.K.; Rajanikant, G.K. Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors. J. Enzyme Inhib. Med. Chem., 2012, 27(4), 558-570.
[37]
Dixon, S.L.; Duan, J.; Smith, E.; Von Bargen, C.D.; Sherman, W.; Repasky, M.P. AutoQSAR: An automated machine learning tool for best-practice quantitative structure-activity relationship modeling. Future Med. Chem., 2016, 8(15), 1825-1839.
[38]
Berk, R. The formalities of multiple regression; SAGE Publications Ltd: London, 2003, pp. 103-110.
[39]
Rogers, D.; Hopfinger, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci., 1994, 34(4), 854-866.
[40]
Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22(1), 69-77.
[41]
Roy, P.P.; Roy, K. On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci., 2008, 27(3), 302-313.
[42]
Veerasamy, R.; Rajak, H.; Jain, A.; Sivadasan, S.; Varghese, C.P.; Agrawal, R.K. Validation of QSAR models-strategies and importance. Int. J. Drug Des. Discov., 2011, 3, 511-519.
[43]
Sharma, M.K.; Murumkar, P.R.; Kuang, G.; Tang, Y.; Yadav, M.R. Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques. RSC Advances, 2016, 6(2), 1466-1483.
[44]
Ojha, P.K.; Mitra, I.; Das, R.N.; Roy, K. Further exploring r m 2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst., 2011, 107(1), 194-205.
[45]
Patel, P.; Singh, A.; Patel, V.K.; Jain, D.K.; Veerasamy, R.; Rajak, H. Pharmacophore Based 3D-QSAR, virtual screening and docking studies on novel series of HDAC inhibitors with thiophen linker as anticancer agents. Comb. Chem. High Throughput Screen., 2016, 19(9), 735-751.
[46]
Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J. Comput. Chem., 2010, 31(11), 2109-2125.
[47]
Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: The prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys., 2016, 18(18), 12964-12975.
[48]
Rajamanikandan, S.; Srinivasan, P. Pharmacophore modeling and structure-based virtual screening to identify potent inhibitors targeting LuxP of Vibrio harveyi. J. Recept. Signal Transduct. Res., 2016, 36(6), 617-632.
[49]
Govind, N.; Petersen, M.; Fitzgerald, G.; King-Smith, D.; Andzelm, J. A generalized synchronous transit method for transition state location. Comput. Mater. Sci., 2003, 28(2), 250-258.
[50]
Truchon, J.F.; Bayly, C.I. Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model., 2007, 47(2), 488-508.
[51]
Xu, L.; Sun, H.; Li, Y.; Wang, J.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models. J. Phys. Chem. B, 2013, 117(28), 8408-8421.
[52]
Sun, H.; Li, Y.; Shen, M.; Tian, S.; Xu, L.; Pan, P.; Guan, Y.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys. Chem. Chem. Phys., 2014, 16(40), 22035-22045.
[53]
Sun, H.; Li, Y.; Tian, S.; Xu, L.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys., 2014, 16(31), 16719-16729.
[54]
Chen, F.; Liu, H.; Sun, H.; Pan, P.; Li, Y.; Li, D.; Hou, T. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking. Phys. Chem. Chem. Phys., 2016, 18(32), 22129-22139.
[55]
Tripathi, S.K.; Selvaraj, C.; Singh, S.K.; Reddy, K.K. Molecular docking, QPLD, and ADME prediction studies on HIV-1 integrase leads. Med. Chem. Res., 2012, 21(12), 4239-4251.
[56]
Kroemer, R.T.; Vulpetti, A.; McDonald, J.J.; Rohrer, D.C.; Trosset, J-Y.; Giordanetto, F.; Cotesta, S.; McMartin, C.; Kihlén, M.; Stouten, P.F. Assessment of docking poses: Interactions-based accuracy classification (IBAC) versus crystal structure deviations. J. Chem. Inf. Comput. Sci., 2004, 44(3), 871-881.
[57]
Sakkiah, S.; Arooj, M.; Kumar, M.R.; Eom, S.H.; Lee, K.W. Identification of inhibitor binding site in human sirtuin 2 using molecular docking and dynamics simulations. PLoS One, 2013, 8(1), e51429.
[58]
Kaufman, J.J. Quantum chemical and physicochemical influences on structure-activity relations and drug design. Int. J. Quantum Chem., 1979, 16(2), 221-241.
[59]
Luque, F.J.; López, J.M.; Orozco, M. Perspective on electrostatic interactions of a solute with a continuum. A direct utilization of ab initio molecular potentials for the prevision of solvent effects. In: Theoretical Chemistry Accounts; Springer, 2000; pp. 343-345.