Login Register Cart 0
Generic placeholder image

Anti-Cancer Agents in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1871-5206
ISSN (Online): 1875-5992

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

Author(s): Rahman Abdizadeh, Esfandiar Heidarian, Farzin Hadizadeh and Tooba Abdizadeh*

Volume 21, Issue 8, 2021

Published on: 21 July, 2020

Page: [987 - 1018] Pages: 32

DOI: 10.2174/1871520620666200721134010

Price: $65

Abstract

Background: Background: Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

Objective: In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

Methods: The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q2), noncross- validated correlation coefficient (r2ncv) and predicted correlation coefficient (r2pred) of the training and test set molecules, respectively.

Results: The CoMFA (q2, 0.670; r2ncv, 0.930; r2pred, 0.968), CoMFA-RF (q2, 0.694; r2ncr, 0.926; r2pred, 0.927), CoMSIA (q2, 0.834; r2ncv, 0.956; r2pred, 0.958) and HQSAR models (q2, 0.854; r2ncv, 0.900; r2pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

Conclusion: These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

Keywords: Lysine-specific demethylase 1, QSAR, tranylcypromine, molecular docking, molecular dynamics simulations.

« Previous Next »
Graphical Abstract

[1]
Wu, F.; Zhou, C.; Yao, Y.; Wei, L.; Feng, Z.; Deng, L.; Song, Y. 3-(Piperidin-4-ylmethoxy) pyridine containing compounds are potent inhibitors of lysine specific demethylase 1. J. Med. Chem., 2016, 59(1), 253-263.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01361] [PMID: 26652247]
[2]
Kakizawa, T.; Ota, Y.; Itoh, Y.; Tsumoto, H.; Suzuki, T. Histone H3 peptide based LSD1-selective inhibitors. Bioorg. Med. Chem. Lett., 2015, 25(9), 1925-1928.
[http://dx.doi.org/10.1016/j.bmcl.2015.03.030] [PMID: 25827526]
[3]
Li, Z-R.; Wang, S.; Yang, L.; Yuan, X-H.; Suo, F-Z.; Yu, B.; Liu, H-M. Experience-Based Discovery (EBD) of aryl hydrazines as new scaffolds for the development of LSD1/KDM1A inhibitors. Eur. J. Med. Chem., 2019, 166, 432-444.
[http://dx.doi.org/10.1016/j.ejmech.2019.01.075] [PMID: 30739825]
[4]
Shi, Y.; Lan, F.; Matson, C.; Mulligan, P.; Whetstine, J.R.; Cole, P.A.; Casero, R.A.; Shi, Y. Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell, 2004, 119(7), 941-953.
[http://dx.doi.org/10.1016/j.cell.2004.12.012] [PMID: 15620353]
[5]
Nie, Z.; Shi, L.; Lai, C.; Severin, C.; Xu, J.; Del Rosario, J.R.; Stansfield, R.K.; Cho, R.W.; Kanouni, T.; Veal, J.M.; Stafford, J.A.; Chen, Y.K. Structure-based design and discovery of potent and selective Lysine-Specific Demethylase 1 (LSD1) inhibitors. Bioorg. Med. Chem. Lett., 2019, 29(1), 103-106.
[http://dx.doi.org/10.1016/j.bmcl.2018.11.001] [PMID: 30409536]
[6]
Benelkebir, H.; Hodgkinson, C.; Duriez, P.J.; Hayden, A.L.; Bulleid, R.A.; Crabb, S.J.; Packham, G.; Ganesan, A. Enantioselective synthesis of tranylcypromine analogues as Lysine Demethylase (LSD1) inhibitors. Bioorg. Med. Chem., 2011, 19(12), 3709-3716.
[http://dx.doi.org/10.1016/j.bmc.2011.02.017] [PMID: 21382717]
[7]
Huang, J.; Sengupta, R.; Espejo, A.B.; Lee, M.G.; Dorsey, J.A.; Richter, M.; Opravil, S.; Shiekhattar, R.; Bedford, M.T.; Jenuwein, T.; Berger, S.L. p53 is regulated by the lysine demethylase LSD1. Nature, 2007, 449(7158), 105-108.
[http://dx.doi.org/10.1038/nature06092] [PMID: 17805299]
[8]
Yang, J.; Huang, J.; Dasgupta, M.; Sears, N.; Miyagi, M.; Wang, B.; Chance, M.R.; Chen, X.; Du, Y.; Wang, Y.; An, L.; Wang, Q.; Lu, T.; Zhang, X.; Wang, Z.; Stark, G.R. Reversible methylation of promoter-bound STAT3 by histone-modifying enzymes. Proc. Natl. Acad. Sci. USA, 2010, 107(50), 21499-21504.
[http://dx.doi.org/10.1073/pnas.1016147107] [PMID: 21098664]
[9]
Kontaki, H.; Talianidis, I. Lysine methylation regulates E2F1-induced cell death. Mol. Cell, 2010, 39(1), 152-160.
[http://dx.doi.org/10.1016/j.molcel.2010.06.006] [PMID: 20603083]
[10]
Wang, J.; Hevi, S.; Kurash, J.K.; Lei, H.; Gay, F.; Bajko, J.; Su, H.; Sun, W.; Chang, H.; Xu, G.; Gaudet, F.; Li, E.; Chen, T. The lysine demethylase LSD1 (KDM1) is required for maintenance of global DNA methylation. Nat. Genet., 2009, 41(1), 125-129.
[http://dx.doi.org/10.1038/ng.268] [PMID: 19098913]
[11]
Cho, H-S.; Suzuki, T.; Dohmae, N.; Hayami, S.; Unoki, M.; Yoshimatsu, M.; Toyokawa, G.; Takawa, M.; Chen, T.; Kurash, J.K.; Field, H.I.; Ponder, B.A.; Nakamura, Y.; Hamamoto, R. Demethylation of RB regulator MYPT1 by histone demethylase LSD1 promotes cell cycle progression in cancer cells. Cancer Res., 2011, 71(3), 655-660.
[http://dx.doi.org/10.1158/0008-5472.CAN-10-2446] [PMID: 21115810]
[12]
Wang, S.; Li, Z-R.; Suo, F-Z.; Yuan, X-H.; Yu, B.; Liu, H-M. Synthesis, structure-activity relationship studies and biological characterization of new [1,2,4]triazolo[1,5-a]pyrimidine-based LSD1/KDM1A inhibitors. Eur. J. Med. Chem., 2019, 167, 388-401.
[http://dx.doi.org/10.1016/j.ejmech.2019.02.039] [PMID: 30780087]
[13]
Lv, T.; Yuan, D.; Miao, X.; Lv, Y.; Zhan, P.; Shen, X.; Song, Y. Over-expression of LSD1 promotes proliferation, migration and invasion in non-small cell lung cancer. PLoS One, 2012, 7(4)e35065
[http://dx.doi.org/10.1371/journal.pone.0035065] [PMID: 22493729]
[14]
Sorna, V.; Theisen, E.R.; Stephens, B.; Warner, S.L.; Bearss, D.J.; Vankayalapati, H.; Sharma, S. High-throughput virtual screening identifies novel N′-(1-phenylethylidene)-benzohydrazides as potent, specific, and reversible LSD1 inhibitors. J. Med. Chem., 2013, 56(23), 9496-9508.
[http://dx.doi.org/10.1021/jm400870h] [PMID: 24237195]
[15]
Yu, Y.; Wang, B.; Zhang, K.; Lei, Z.; Guo, Y.; Xiao, H.; Wang, J.; Fan, L.; Lan, C.; Wei, Y.; Ma, Q.; Lin, L.; Mao, C.; Yang, X.; Chen, X.; Li, Y.; Bai, Y.; Chen, D. High expression of lysine-specific demethylase 1 correlates with poor prognosis of patients with esophageal squamous cell carcinoma. Biochem. Biophys. Res. Commun., 2013, 437(2), 192-198.
[http://dx.doi.org/10.1016/j.bbrc.2013.05.123] [PMID: 23747727]
[16]
Ma, Q-S.; Yao, Y.; Zheng, Y-C.; Feng, S.; Chang, J.; Yu, B.; Liu, H-M. Ligand-based design, synthesis and biological evaluation of xanthine derivatives as LSD1/KDM1A inhibitors. Eur. J. Med. Chem., 2019, 162, 555-567.
[http://dx.doi.org/10.1016/j.ejmech.2018.11.035] [PMID: 30472603]
[17]
Fu, X.; Zhang, P.; Yu, B. Advances toward LSD1 inhibitors for cancer therapy. Future Med. Chem., 2017, 9(11), 1227-1242.
[http://dx.doi.org/10.4155/fmc-2017-0068] [PMID: 28722477]
[18]
Rodriguez, V.; Valente, S.; Rovida, S.; Rotili, D.; Stazi, G.; Lucidi, A.; Ciossani, G.; Mattevi, A.; Botrugno, O.A.; Dessanti, P. Pyrrole-and indole-containing tranylcypromine derivatives as novel lysine-specific demethylase 1 inhibitors active on cancer cells. MedChemComm, 2015, 6(4), 665-670.
[http://dx.doi.org/10.1039/C4MD00507D]
[19]
Mohammad, H.P.; Smitheman, K.N.; Kamat, C.D.; Soong, D.; Federowicz, K.E.; Van Aller, G.S.; Schneck, J.L.; Carson, J.D.; Liu, Y.; Butticello, M.; Bonnette, W.G.; Gorman, S.A.; Degenhardt, Y.; Bai, Y.; McCabe, M.T.; Pappalardi, M.B.; Kasparec, J.; Tian, X.; McNulty, K.C.; Rouse, M.; McDevitt, P.; Ho, T.; Crouthamel, M.; Hart, T.K.; Concha, N.O.; McHugh, C.F.; Miller, W.H.; Dhanak, D.; Tummino, P.J.; Carpenter, C.L.; Johnson, N.W.; Hann, C.L.; Kruger, R.G. A DNA hypomethylation signature predicts antitumor activity of LSD1 inhibitors in SCLC. Cancer Cell, 2015, 28(1), 57-69.
[http://dx.doi.org/10.1016/j.ccell.2015.06.002] [PMID: 26175415]
[20]
Kruger, R.G.; Mohammad, H.; Smitheman, K.; Cusan, M.; Liu, Y.; Pappalardi, M.; Federowicz, K.; Van Aller, G.; Kasparec, J.; Tian, X.; Suarez, D. Inhibition of LSD1 as a therapeutic strategy for the treatment of acute myeloid leukemia. Blood, 2013, 122, 3964.
[http://dx.doi.org/10.1182/blood.V122.21.3964.3964]
[21]
Milletti, F.; Cheng, W-Y.; Maes, T.; Lunardi, S.; DeMario, M.; Pierceall, W.E.; Mack, F. Neuroendocrine gene transcript expression is associated with efficacy to lysine-specific demethylase-1 inhibitor RG6016 in small cell lung cancer-derived cell lines. Cancer Res., 2016, 76(14), 4708.
[http://dx.doi.org/10.1158/1538-7445.AM2016-4708]
[22]
Prusevich, P.; Kalin, J.H.; Ming, S.A.; Basso, M.; Givens, J.; Li, X.; Hu, J.; Taylor, M.S.; Cieniewicz, A.M.; Hsiao, P-Y.; Huang, R.; Roberson, H.; Adejola, N.; Avery, L.B.; Casero, R.A., Jr; Taverna, S.D.; Qian, J.; Tackett, A.J.; Ratan, R.R.; McDonald, O.G.; Feinberg, A.P.; Cole, P.A. A selective phenelzine analogue inhibitor of histone demethylase LSD1. ACS Chem. Biol., 2014, 9(6), 1284-1293.
[http://dx.doi.org/10.1021/cb500018s] [PMID: 24707965]
[23]
Wang, M.; Liu, X.; Guo, J.; Weng, X.; Jiang, G.; Wang, Z.; He, L. Inhibition of LSD1 by Pargyline inhibited process of EMT and delayed progression of prostate cancer in vivo. Biochem. Biophys. Res. Commun., 2015, 467(2), 310-315.
[http://dx.doi.org/10.1016/j.bbrc.2015.09.164] [PMID: 26435505]
[24]
Magliulo, D.; Bernardi, R.; Messina, S. Lysine-specific demethylase 1A as a promising target in acute myeloid leukemia. Front. Oncol., 2018, 8, 255-255.
[http://dx.doi.org/10.3389/fonc.2018.00255] [PMID: 30073149]
[25]
Yang, G-J.; Lei, P-M.; Wong, S-Y.; Ma, D-L.; Leung, C-H. Pharmacological inhibition of LSD1 for cancer treatment. Molecules, 2018, 23(12), 3194.
[http://dx.doi.org/10.3390/molecules23123194] [PMID: 30518104]
[26]
Zheng, Y-C.; Duan, Y-C.; Ma, J-L.; Xu, R-M.; Zi, X.; Lv, W-L.; Wang, M-M.; Ye, X-W.; Zhu, S.; Mobley, D.; Zhu, Y.Y.; Wang, J.W.; Li, J.F.; Wang, Z.R.; Zhao, W.; Liu, H.M. Triazole-dithiocarbamate based selective Lysine Specific Demethylase 1 (LSD1) inactivators inhibit gastric cancer cell growth, invasion, and migration. J. Med. Chem., 2013, 56(21), 8543-8560.
[http://dx.doi.org/10.1021/jm401002r] [PMID: 24131029]
[27]
Dulla, B.; Kirla, K.T.; Rathore, V.; Deora, G.S.; Kavela, S.; Maddika, S.; Chatti, K.; Reiser, O.; Iqbal, J.; Pal, M. Synthesis and evaluation of 3-amino/guanidine substituted phenyl oxazoles as a novel class of LSD1 inhibitors with anti-proliferative properties. Org. Biomol. Chem., 2013, 11(19), 3103-3107.
[http://dx.doi.org/10.1039/c3ob40217g] [PMID: 23575971]
[28]
Duan, Y-C.; Guan, Y-Y.; Zhai, X-Y.; Ding, L-N.; Qin, W-P.; Shen, D-D.; Liu, X-Q.; Sun, X-D.; Zheng, Y-C.; Liu, H-M. Discovery of resveratrol derivatives as novel LSD1 inhibitors: Design, synthesis and their biological evaluation. Eur. J. Med. Chem., 2017, 126, 246-258.
[http://dx.doi.org/10.1016/j.ejmech.2016.11.035] [PMID: 27888721]
[29]
Wang, J.; Lu, F.; Ren, Q.; Sun, H.; Xu, Z.; Lan, R.; Liu, Y.; Ward, D.; Quan, J.; Ye, T.; Zhang, H. Novel histone demethylase LSD1 inhibitors selectively target cancer cells with pluripotent stem cell properties. Cancer Res., 2011, 71(23), 7238-7249.
[http://dx.doi.org/10.1158/0008-5472.CAN-11-0896] [PMID: 21975933]
[30]
Hazeldine, S.; Pachaiyappan, B.; Steinbergs, N.; Nowotarski, S.; Hanson, A.S.; Casero, R.A., Jr; Woster, P.M. Low molecular weight amidoximes that act as potent inhibitors of lysine-specific demethylase 1. J. Med. Chem., 2012, 55(17), 7378-7391.
[http://dx.doi.org/10.1021/jm3002845] [PMID: 22876979]
[31]
Ji, Y-Y.; Lin, S-D.; Wang, Y-J.; Su, M-B.; Zhang, W.; Gunosewoyo, H.; Yang, F.; Li, J.; Tang, J.; Zhou, Y-B. Tying up tranylcypromine: Novel selective histone Lysine Specific Demethylase 1 (LSD1) inhibitors. Eur. J. Med. Chem., 2017, 141, 101-112.
[http://dx.doi.org/10.1016/j.ejmech.2017.09.073]
[32]
Kubinyi, H. QSAR and 3D QSAR in drug design Part 1: Methodology. Drug Discov. Today, 1997, 2(11), 457-467.
[http://dx.doi.org/10.1016/S1359-6446(97)01079-9]
[33]
Kubinyi, H. QSAR and 3D QSAR in drug design Part 2: Applications and problems. Drug Discov. Today, 1997, 2(12), 538-546.
[http://dx.doi.org/10.1016/S1359-6446(97)01084-2]
[34]
Frimayanti, N.; Lee, V.S.; Zain, S.M.; Wahab, H.A.; Rahman, N.A., II 3D-QSAR, and pharmacophore studies on thiazolidine-4-carboxylic acid derivatives as neuraminidase inhibitors in H3N2 influenza virus. Med. Chem. Res., 2014, 23(3), 1447-1453.
[http://dx.doi.org/10.1007/s00044-013-0750-x]
[35]
Akamatsu, M. Current state and perspectives of 3D-QSAR. Curr. Top. Med. Chem., 2002, 2(12), 1381-1394.
[http://dx.doi.org/10.2174/1568026023392887] [PMID: 12470286]
[36]
Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design--a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID: 19929826]
[37]
Zhu, J.; Ke, K.; Xu, L.; Jin, J. Theoretical studies on the selectivity mechanisms of PI3Kδ inhibition with marketed idelalisib and its derivatives by 3D-QSAR, molecular docking, and molecular dynamics simulation. J. Mol. Model., 2019, 25(8), 242.
[http://dx.doi.org/10.1007/s00894-019-4129-x] [PMID: 31338599]
[38]
Abdizadeh, T.; Ghodsi, R.; Hadizadeh, F. 3D-QSAR (CoMFA, CoMSIA) and molecular docking studies on histone deacetylase 1 selective inhibitors. Rec. Pat. Anticancer Drug Discov., 2017, 12(4), 365-383.
[http://dx.doi.org/10.2174/1574892812666170508125927] [PMID: 28482791]
[39]
Wang, Z-Z.; Ma, C-Y.; Yang, J.; Gao, Q-B.; Sun, X-D.; Ding, L.; Liu, H-M. Investigating the binding mechanism of (4-Cyanophenyl) glycine derivatives as reversible LSD1 by 3D-QSAR, molecular docking and molecular dynamics simulations. J. Mol. Struct., 2019, 698, 707-715.
[http://dx.doi.org/10.1016/j.molstruc.2018.08.029]
[40]
Wang, Z-Z.; Yang, J.; Sun, X-D.; Ma, C-Y.; Gao, Q-B.; Ding, L.; Liu, H-M. Probing the binding mechanism of substituted pyridine derivatives as effective and selective lysine-specific demethylase 1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations. J. Biomol. Struct. Dyn., 2019, 37(13), 3482-3495.
[http://dx.doi.org/10.1080/07391102.2018.1518158] [PMID: 30175693]
[41]
Ruslin, R.; Amelia, R.; Yamin, Y.; Megantara, S.; Wu, C.; Arba, M. 3D-QSAR, molecular docking, and dynamics simulation of quinazoline-phosphoramidate mustard conjugates as EGFR inhibitor J. Appl. Pharm. Sci, 2019, 9(01), 089-097.
[42]
Borrello, M.T.; Schinor, B.; Bartels, K.; Benelkebir, H.; Pereira, S.; Al-Jamal, W.T.; Douglas, L.; Duriez, P.J.; Packham, G.; Haufe, G.; Ganesan, A. Fluorinated tranylcypromine analogues as inhibitors of Lysine-Specific Demethylase 1 (LSD1, KDM1A). Bioorg. Med. Chem. Lett., 2017, 27(10), 2099-2101.
[http://dx.doi.org/10.1016/j.bmcl.2017.03.081] [PMID: 28390942]
[43]
Sun, K.; Peng, J-D.; Suo, F-Z.; Zhang, T.; Fu, Y-D.; Zheng, Y-C.; Liu, H-M. Discovery of tranylcypromine analogs with an acylhydrazone substituent as LSD1 inactivators: Design, synthesis and their biological evaluation. Bioorg. Med. Chem. Lett., 2017, 27(22), 5036-5039.
[http://dx.doi.org/10.1016/j.bmcl.2017.10.003] [PMID: 29037950]
[44]
Schulz-Fincke, J.; Hau, M.; Barth, J.; Robaa, D.; Willmann, D.; Kürner, A.; Haas, J.; Greve, G.; Haydn, T.; Fulda, S.; Lübbert, M.; Lüdeke, S.; Berg, T.; Sippl, W.; Schüle, R.; Jung, M. Structure-activity studies on N-Substituted tranylcypromine derivatives lead to selective inhibitors of Lysine Specific Demethylase 1 (LSD1) and potent inducers of leukemic cell differentiation. Eur. J. Med. Chem., 2018, 144, 52-67.
[http://dx.doi.org/10.1016/j.ejmech.2017.12.001] [PMID: 29247860]
[45]
Clark, M.; Cramer, R.D., III; Van Opdenbosch, N. Validation of the general purpose Tripos 5.2 force field. J. Comput. Chem., 1989, 10(8), 982-1012.
[http://dx.doi.org/10.1002/jcc.540100804]
[46]
Cramer, R.D., III; Bunce, J.D.; Patterson, D.E.; Frank, I.E. Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant. Struct. Act. Relat., 1988, 7(1), 18-25.
[http://dx.doi.org/10.1002/qsar.19880070105]
[47]
Kellogg, G.E.; Semus, S.F.; Abraham, D.J. HINT: A new method of empirical hydrophobic field calculation for CoMFA. J. Comput. Aided Mol. Des., 1991, 5(6), 545-552.
[http://dx.doi.org/10.1007/BF00135313] [PMID: 1818090]
[48]
Borisa, A.; Bhatt, H. 3D-QSAR (CoMFA, CoMFA-RG, CoMSIA) and molecular docking study of thienopyrimidine and thienopyridine derivatives to explore structural requirements for aurora-B kinase inhibition. Eur. J. Pharm. Sci., 2015, 79, 1-12.
[http://dx.doi.org/10.1016/j.ejps.2015.08.017] [PMID: 26343315]
[49]
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37(24), 4130-4146.
[http://dx.doi.org/10.1021/jm00050a010] [PMID: 7990113]
[50]
Hao, M.; Li, Y.; Wang, Y.; Yan, Y.; Zhang, S.; Li, G.; Yang, L. Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation. J. Chem. Inf. Model., 2011, 51(10), 2560-2572.
[http://dx.doi.org/10.1021/ci2002878] [PMID: 21923153]
[51]
Dunn, W., III; Wold, S.; Edlund, U.; Hellberg, S.; Gasteiger, J. Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method. Quant. Struct. Act. Relat., 1984, 3(4), 131-137.
[http://dx.doi.org/10.1002/qsar.19840030402]
[52]
Geladi, P. Notes on the history and nature of Partial Least Squares (PLS) modelling. J. Chemometr., 1988, 2(4), 231-246.
[http://dx.doi.org/10.1002/cem.1180020403]
[53]
Kubinyi, H.; Martin, Y.C.; Folkers, G. 3D QSAR in Drug Design: Volume 1: Theory Methods and Applications; Springer Science Business Media: Germany , 1993; Vol. 1, .
[54]
Bush, B.L.; Nachbar, R.B., Jr Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J. Comput. Aided Mol. Des., 1993, 7(5), 587-619.
[http://dx.doi.org/10.1007/BF00124364] [PMID: 8294948]
[55]
Lowis, D.R. HQSAR: A new, highly predictive QSAR technique. Tripos. Tech. Notes., 1997, 1(5), 17.
[http://dx.doi.org/10.3390/ecsoc-1-02064]
[56]
Maltarollo, V.G.; Honório, K.M.; Emery, F.S.; Ganesan, A.; Trossini, G.H. Hologram quantitative structure-activity relationship and comparative molecular interaction field analysis of aminothiazole and thiazolesulfonamide as reversible LSD1 inhibitors. Future Med. Chem., 2015, 7(11), 1381-1394.
[http://dx.doi.org/10.4155/fmc.15.68] [PMID: 26230878]
[57]
Zheng, M.; Yu, K.; Liu, H.; Luo, X.; Chen, K.; Zhu, W.; Jiang, H. QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR. J. Comput. Aided Mol. Des., 2006, 20(9), 549-566.
[http://dx.doi.org/10.1007/s10822-006-9080-0] [PMID: 17103017]
[58]
Xu, Y.; He, Z.; Liu, H.; Chen, Y.; Gao, Y.; Zhang, S.; Wang, M.; Lu, X.; Wang, C.; Zhao, Z.; Liu, Y.; Zhao, J.; Yu, Y.; Yang, M. 3D-QSAR, molecular docking, and molecular dynamics simulations study of thieno[3,2-b]pyrrole-5-carboxamide derivatives as LSD1 inhibitors. RSC Advances, 2020, 10, 6927-6943.
[http://dx.doi.org/10.1039/C9RA10085G]
[59]
Araujo, S.C.; Maltarollo, V.G.; Honorio, K.M. Computational studies of TGF-βRI (ALK-5) inhibitors: Analysis of the binding interactions between ligand-receptor using 2D and 3D techniques. Eur. J. Pharm. Sci., 2013, 49(4), 542-549.
[http://dx.doi.org/10.1016/j.ejps.2013.05.015] [PMID: 23727056]
[60]
Kronenberger, T.; Asse, L.R., Jr; Wrenger, C.; Trossini, G.H.; Honorio, K.M.; Maltarollo, V.G. Studies of Staphylococcus aureus FabI inhibitors: Fragment-based approach based on holographic structure-activity relationship analyses. Future Med. Chem., 2017, 9(2), 135-151.
[http://dx.doi.org/10.4155/fmc-2016-0179]
[61]
Gomes, R.A.; Genesi, G.L.; Maltarollo, V.G.; Trossini, G.H.G. Quantitative structure-activity relationships (HQSAR, CoMFA, and CoMSIA) studies for COX-2 selective inhibitors. J. Biomol. Struct. Dyn., 2017, 35(7), 1436-1445.
[http://dx.doi.org/10.1080/07391102.2016.1185379] [PMID: 27145042]
[62]
Ståhle, L.; Wold, S. Partial least squares analysiswith cross-validation for the two-class problem: A Monte Carlo study. J. Chemometr., 1987, 1(3), 185-196.
[http://dx.doi.org/10.1002/cem.1180010306]
[63]
Shukla, A.; Tyagi, R.; Meena, S.; Datta, D.; Kumar Srivastava, S.; Khan, F. 2D-and 3D-QSAR modelling molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line. J. Biomol. Struct. Dyn., 2020, 38(1), 168-185.
[64]
Vrontaki, E.; Melagraki, G.; Voskou, S.; Phylactides, M.S.; Mavromoustakos, T.; Kleanthous, M.; Afantitis, A. Development of a predictive pharmacophore model and a 3D-QSAR study for an in silico screening of new potent Bcr-Abl kinase inhibitors. Mini Rev. Med. Chem., 2017, 17(3), 188-204.
[http://dx.doi.org/10.2174/1389557516999160629101709] [PMID: 28143387]
[65]
Meng, L.; Feng, K.; Ren, Y. Molecular modelling studies of tricyclic triazinone analogues as potential PKC-θ inhibitors through combined QSAR, molecular docking and molecular dynamics simulations techniques. J. Taiwan Inst. Chem. Eng., 2018, 91, 155-175.
[http://dx.doi.org/10.1016/j.jtice.2018.06.017]
[66]
Kearns, M.; Ron, D. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput., 1999, 11(6), 1427-1453.
[http://dx.doi.org/10.1162/089976699300016304] [PMID: 10423502]
[67]
Wold, S. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 1978, 20(4), 397-405.
[http://dx.doi.org/10.1080/00401706.1978.10489693]
[68]
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]
[69]
Rácz, A.; Bajusz, D.; Héberger, K. Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters. SAR QSAR Environ. Res., 2015, 26(7-9), 683-700.
[http://dx.doi.org/10.1080/1062936X.2015.1084647] [PMID: 26434574]
[70]
Consonni, V.; Ballabio, D.; Todeschini, R. Comments on the definition of the Q2 parameter for QSAR validation. J. Chem. Inf. Model., 2009, 49(7), 1669-1678.
[http://dx.doi.org/10.1021/ci900115y] [PMID: 19527034]
[71]
Wang, Z.; Cheng, L.; Kai, Z.; Wu, F.; Liu, Z.; Cai, M. Molecular modeling studies of atorvastatin analogues as HMGR inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations. Bioorg. Med. Chem. Lett., 2014, 24(16), 3869-3876.
[http://dx.doi.org/10.1016/j.bmcl.2014.06.055] [PMID: 25022881]
[72]
Chhatbar, D.M.; Chaube, U.J.; Vyas, V.K.; Bhatt, H.G. CoMFA, CoMSIA, Topomer CoMFA, HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer. Comput. Biol. Chem., 2019, 80, 351-363.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.04.017] [PMID: 31085426]
[73]
Lorca, M.; Morales-Verdejo, C.; Vásquez-Velásquez, D.; Andrades-Lagos, J.; Campanini-Salinas, J.; Soto-Delgado, J.; Recabarren-Gajardo, G.; Mella, J. Structure-activity relationships based on 3D-QSAR CoMFA/CoMSIA and design of aryloxypropanol-amine agonists with selectivity for the human β3-adrenergic receptor and anti-obesity and anti-diabetic profiles. Molecules, 2018, 23(5), 1-21.
[http://dx.doi.org/10.3390/molecules23051191] [PMID: 29772697]
[74]
Rücker, C.; Rücker, G.; Meringer, M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model., 2007, 47(6), 2345-2357.
[http://dx.doi.org/10.1021/ci700157b] [PMID: 17880194]
[75]
Agrawal, V.K.; Khadikar, P.V. QSAR prediction of toxicity of nitrobenzenes. Bioorg. Med. Chem., 2001, 9(11), 3035-3040.
[http://dx.doi.org/10.1016/S0968-0896(01)00211-5] [PMID: 11597486]
[76]
Duchowicz, P.R. Linear regression QSAR models for polo-like kinase-1 inhibitors. Cells, 2018, 7(2), 13.
[http://dx.doi.org/10.3390/cells7020013] [PMID: 29443884]
[77]
Weaver, S.; Gleeson, M.P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model., 2008, 26(8), 1315-1326.
[http://dx.doi.org/10.1016/j.jmgm.2008.01.002] [PMID: 18328754]
[78]
Kaneko, H.; Funatsu, K. Applicability domain based on ensemble learning in classification and regression analyses. J. Chem. Inf. Model., 2014, 54(9), 2469-2482.
[http://dx.doi.org/10.1021/ci500364e] [PMID: 25119661]
[79]
Veerasamy, R.; Rajak, H.; Jain, A.; Sivadasan, S.; Varghese, C.P.; Agrawal, R.K. Validation of QSAR models-strategies and importance. J. Drug Des. Discov., 2011, 2, 511-519.
[http://dx.doi.org/10.1016/B978-0-12-801505-6.00007-7]
[80]
Yang, X.; Liu, H.; Yang, Q.; Liu, J.; Chen, J.; Shi, L. Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships. Chemosphere, 2016, 163, 373-381.
[http://dx.doi.org/10.1016/j.chemosphere.2016.08.062] [PMID: 27561732]
[81]
Lei, T.; Chen, F.; Liu, H.; Sun, H.; Kang, Y.; Li, D.; Li, Y.; Hou, T. ADMET Evaluation in drug discovery. Part 17: Development of quantitative and qualitative prediction models for chemical-induced respiratory toxicity. Mol. Pharm., 2017, 14(7), 2407-2421.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00317] [PMID: 28595388]
[82]
Zheng, Y.C.; Yu, B.; Chen, Z.S.; Liu, Y.; Liu, H.M. TCPs: Privileged scaffolds for identifying potent LSD1 inhibitors for cancer therapy. Epigenomics, 2016, 8(5), 651-666.
[http://dx.doi.org/10.2217/epi-2015-0002] [PMID: 27102879]
[83]
Fang, Y.; Liao, G.; Yu, B. LSD1/KDM1A inhibitors in clinical trials: Advances and prospects. J. Hematol. Oncol., 2019, 12(1), 129.
[http://dx.doi.org/10.1186/s13045-019-0811-9] [PMID: 31801559]
[84]
Niwa, H.; Umehara, T. Structural insight into inhibitors of flavin adenine dinucleotide-dependent lysine demethylases. Epigenetics, 2017, 12(5), 340-352.
[http://dx.doi.org/10.1080/15592294.2017.1290032] [PMID: 28277979]
[85]
Mimasu, S.; Umezawa, N.; Sato, S.; Higuchi, T.; Umehara, T.; Yokoyama, S. Structurally designed trans-2-phenylcyclopropyl-amine derivatives potently inhibit histone demethylase LSD1/KDM1. Biochemistry, 2010, 49(30), 6494-6503.
[http://dx.doi.org/10.1021/bi100299r] [PMID: 20568732]
[86]
Mimasu, S.; Sengoku, T.; Fukuzawa, S.; Umehara, T.; Yokoyama, S. Crystal structure of histone demethylase LSD1 and tranylcypromine at 2.25 A. Biochem. Biophys. Res. Commun., 2008, 366(1), 15-22.
[http://dx.doi.org/10.1016/j.bbrc.2007.11.066] [PMID: 18039463]

Rights & Permissions Print Cite
Article Metrics
40
1
© 2024 Bentham Science Publishers | Privacy Policy