Generic placeholder image

Anti-Cancer Agents in Medicinal Chemistry

Editor-in-Chief

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

Research Article

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction

Author(s): Amrutha Krishnan K.*, Sudha George Valavi and Amitha Joy

Volume 24, Issue 4, 2024

Published on: 03 January, 2024

Page: [263 - 279] Pages: 17

DOI: 10.2174/0118715206275566231206094645

Price: $65

Abstract

Background: Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC.

Objectives: Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis.

Methods: In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis.

Results: The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 μM.

Conclusion: This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

Graphical Abstract

[1]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[3]
Spano, J.P.; Lagorce, C.; Atlan, D.; Milano, G.; Domont, J.; Benamouzig, R.; Attar, A.; Benichou, J.; Martin, A.; Morere, J.F.; Raphael, M.; Penault-Llorca, F.; Breau, J.L.; Fagard, R.; Khayat, D.; Wind, P. Impact of EGFR expression on colorectal cancer patient prognosis and survival. Ann. Oncol., 2005, 16(1), 102-108.
[http://dx.doi.org/10.1093/annonc/mdi006] [PMID: 15598946]
[4]
Ohashi, K.; Maruvka, Y.E.; Michor, F.; Pao, W. Epidermal growth factor receptor tyrosine kinase inhibitor-resistant disease. J. Clin. Oncol., 2013, 31(8), 1070-1080.
[http://dx.doi.org/10.1200/JCO.2012.43.3912] [PMID: 23401451]
[5]
Frattini, M.; Saletti, P.; Molinari, F.; De Dosso, S. EGFR signaling in colorectal cancer: A clinical perspective. Gastrointest. Cancer, 2015, 21, 21.
[http://dx.doi.org/10.2147/GICTT.S49002]
[6]
Paez, J.G.; Jänne, P.A.; Lee, J.C.; Tracy, S.; Greulich, H.; Gabriel, S.; Herman, P.; Kaye, F.J.; Lindeman, N.; Boggon, T.J.; Naoki, K.; Sasaki, H.; Fujii, Y.; Eck, M.J.; Sellers, W.R.; Johnson, B.E.; Meyerson, M. EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science, 2004, 304(5676), 1497-1500.
[http://dx.doi.org/10.1126/science.1099314] [PMID: 15118125]
[7]
Pao, W.; Miller, V.; Zakowski, M.; Doherty, J.; Politi, K.; Sarkaria, I.; Singh, B.; Heelan, R.; Rusch, V.; Fulton, L.; Mardis, E.; Kupfer, D.; Wilson, R.; Kris, M.; Varmus, H. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl. Acad. Sci., 2004, 101(36), 13306-13311.
[http://dx.doi.org/10.1073/pnas.0405220101] [PMID: 15329413]
[8]
Ogino, S.; Meyerhardt, J.A.; Cantor, M.; Brahmandam, M.; Clark, J.W.; Namgyal, C.; Kawasaki, T.; Kinsella, K.; Michelini, A.L.; Enzinger, P.C.; Kulke, M.H.; Ryan, D.P.; Loda, M.; Fuchs, C.S. Molecular alterations in tumors and response to combination chemotherapy with gefitinib for advanced colorectal cancer. Clin. Cancer Res., 2005, 11(18), 6650-6656.
[http://dx.doi.org/10.1158/1078-0432.CCR-05-0738] [PMID: 16166444]
[9]
Bonomi, P.D.; Buckingham, L.; Coon, J. Selecting patients for treatment with epidermal growth factor tyrosine kinase inhibitors. Clin. Cancer Res., 2007, 13(15), 4606s-4612s.
[http://dx.doi.org/10.1158/1078-0432.CCR-07-0332] [PMID: 17671150]
[10]
Barber, T.D.; Vogelstein, B.; Kinzler, K.W.; Velculescu, V.E. Somatic mutations of EGFR in colorectal cancers and glioblastomas. N. Engl. J. Med., 2004, 351(27), 2883.
[http://dx.doi.org/10.1056/NEJM200412303512724] [PMID: 15625347]
[11]
Pabla, B.; Bissonnette, M.; Konda, V.J. Colon cancer and the epidermal growth factor receptor: Current treatment paradigms, the importance of diet, and the role of chemoprevention. World J. Clin. Oncol., 2015, 6(5), 133-141.
[http://dx.doi.org/10.5306/wjco.v6.i5.133] [PMID: 26468449]
[12]
Zhao, Y.; Ma, J.; Fan, Y.; Wang, Z.; Tian, R.; Ji, W.; Zhang, F.; Niu, R. TGF -β transactivates EGFR and facilitates breast cancer migration and invasion through canonical Smad3 and ERK/Sp1 signaling pathways. Mol. Oncol., 2018, 12(3), 305-321.
[http://dx.doi.org/10.1002/1878-0261.12162] [PMID: 29215776]
[13]
Zhao, H.; Ming, T.; Tang, S.; Ren, S.; Yang, H.; Liu, M.; Tao, Q.; Xu, H. Wnt signaling in colorectal cancer: Pathogenic role and therapeutic target. Mol. Cancer, 2022, 21(1), 144.
[http://dx.doi.org/10.1186/s12943-022-01616-7] [PMID: 35836256]
[14]
Majhi, M; Ali, MA; Limaye, A; Sinha, K; Bairagi, P; Chouksey, M; Shukla, R; Kanwar, N; Hussain, T; Nayarisseri, A; Singh, S K An in silico investigation of potential EGFR inhibitors for the clinical treatment of colorectal cancer. Curr. Topics. Med. Chem., 2018, 18(27), 2355-2366.
[http://dx.doi.org/10.2174/1568026619666181129144107]
[15]
Rothenberg, M.L.; LaFleur, B.; Levy, D.E.; Washington, M.K.; Morgan-Meadows, S.L.; Ramanathan, R.K.; Berlin, J.D.; Benson, A.B., III; Coffey, R.J. Randomized phase II trial of the clinical and biological effects of two dose levels of gefitinib in patients with recurrent colorectal adenocarcinoma. J. Clin. Oncol., 2005, 23(36), 9265-9274.
[http://dx.doi.org/10.1200/JCO.2005.03.0536] [PMID: 16361624]
[16]
Kuo, T.; Cho, C.D.; Halsey, J.; Wakelee, H.A.; Advani, R.H.; Ford, J.M.; Fisher, G.A.; Sikic, B.I. Phase II study of gefitinib, fluorouracil, leucovorin, and oxaliplatin therapy in previously treated patients with metastatic colorectal cancer. J. Clin. Oncol., 2005, 23(24), 5613-5619.
[http://dx.doi.org/10.1200/JCO.2005.08.359] [PMID: 16110021]
[17]
Fisher, G.A.; Kuo, T.; Ramsey, M.; Schwartz, E.; Rouse, R.V.; Cho, C.D.; Halsey, J.; Sikic, B.I. A phase II study of gefitinib, 5-fluorouracil, leucovorin, and oxaliplatin in previously untreated patients with metastatic colorectal cancer. Clin. Cancer Res., 2008, 14(21), 7074-7079.
[http://dx.doi.org/10.1158/1078-0432.CCR-08-1014] [PMID: 18981005]
[18]
Santoro, A.; Comandone, A.; Rimassa, L.; Granetti, C.; Lorusso, V.; Oliva, C.; Ronzoni, M.; Siena, S.; Zuradelli, M.; Mari, E.; Pressiani, T.; Carnaghi, C. A phase II randomized multicenter trial of gefitinib plus FOLFIRI and FOLFIRI alone in patients with metastatic colorectal cancer. Ann. Oncol., 2008, 19(11), 1888-1893.
[http://dx.doi.org/10.1093/annonc/mdn401] [PMID: 18667394]
[19]
Meyerhardt, J.A.; Zhu, A.X.; Enzinger, P.C.; Ryan, D.P.; Clark, J.W.; Kulke, M.H.; Earle, C.C.; Vincitore, M.; Michelini, A.; Sheehan, S.; Fuchs, C.S. Phase II study of capecitabine, oxaliplatin, and erlotinib in previously treated patients with metastastic colorectal cancer. J. Clin. Oncol., 2006, 24(12), 1892-1897.
[http://dx.doi.org/10.1200/JCO.2005.05.3728] [PMID: 16622264]
[20]
Yanagisawa, A.; Kinehara, Y.; Kijima, R.; Tanaka, M.; Ninomiya, R.; Jokoji, R.; Tachibana, I. Metastatic lung tumors from colorectal cancer with EGFR mutations that responded to osimertinib. Intern. Med., 2023, 62(5), 769-773.
[http://dx.doi.org/10.2169/internalmedicine.0002-22] [PMID: 35871578]
[21]
Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol., 2007, 152(1), 21-37.
[http://dx.doi.org/10.1038/sj.bjp.0707306] [PMID: 17549046]
[22]
Samad, A.; Ahammad, F.; Nain, Z.; Alam, R.; Imon, R.R.; Hasan, M.; Rahman, M.S. Designing a multi-epitope vaccine against SARS-CoV-2: An immunoinformatics approach. J. Biomol. Struct. Dyn., 2022, 40(1), 14-30.
[http://dx.doi.org/10.1080/07391102.2020.1792347] [PMID: 32677533]
[23]
Khedkar, S.; Malde, A.; Coutinho, E.; Srivastava, S. Pharmacophore modeling in drug discovery and development: An overview. Med. Chem., 2007, 3(2), 187-197.
[http://dx.doi.org/10.2174/157340607780059521] [PMID: 17348856]
[24]
Opo, F.A.D.M.; Rahman, M.M.; Ahammad, F.; Ahmed, I.; Bhuiyan, M.A.; Asiri, A.M. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep., 2021, 11(1), 4049.
[http://dx.doi.org/10.1038/s41598-021-83626-x] [PMID: 33603068]
[25]
Darvas, F.; Keseru, G.; Papp, A.; Dormán, G.; Urge, L.; Krajcsi, P. In silico and Ex silico ADME approaches for drug discovery. Curr. Top. Med. Chem., 2002, 2(12), 1287-1304.
[http://dx.doi.org/10.2174/1568026023392841] [PMID: 12470281]
[26]
Zheng, S. In silico identification of potent small molecule inhibitors targeting epidermal growth factor receptor 1. J. Cancer Res. Therapeut., 2018, 14(1), 18-23.
[http://dx.doi.org/10.4103/jcrt.JCRT_365_17]
[27]
Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem 2023 update. Nucleic Acids Res., 2023, 51(D1), D1373-D1380.
[http://dx.doi.org/10.1093/nar/gkac956] [PMID: 36305812]
[28]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594.
[http://dx.doi.org/10.1021/jm300687e] [PMID: 22716043]
[29]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform., 2011, 3(1), 33.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]
[30]
Golestanian, S.; Sharifi, A.; Popowicz, G.M.; Azizian, H.; Foroumadi, A.; Szwagierczak, A.; Holak, T.A.; Amanlou, M. Discovery of novel dual inhibitors against Mdm2 and Mdmx proteins by In silico approaches and binding assay. Life Sci., 2016, 145, 240-246.
[http://dx.doi.org/10.1016/j.lfs.2015.12.047] [PMID: 26746660]
[31]
Koes, D.R.; Camacho, C.J. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res., 2012, 40(W1), W409-W414.
[http://dx.doi.org/10.1093/nar/gks378] [PMID: 22553363]
[32]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: A comprehensive resource for In silico drug discovery and exploration. Nucleic Acids Res., 2006, 34(90001), D668-D672.
[http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]
[33]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[34]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[35]
Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res., 2018, 46(W1), W257-W263.
[http://dx.doi.org/10.1093/nar/gky318] [PMID: 29718510]
[36]
Cheng, H.; Nair, S.K.; Murray, B.W.; Almaden, C.; Bailey, S.; Baxi, S.; Behenna, D.; Cho-Schultz, S.; Dalvie, D.; Dinh, D.M.; Edwards, M.P.; Feng, J.L.; Ferre, R.A.; Gajiwala, K.S.; Hemkens, M.D.; Jackson-Fisher, A.; Jalaie, M.; Johnson, T.O.; Kania, R.S.; Kephart, S.; Lafontaine, J.; Lunney, B.; Liu, K.K.C.; Liu, Z.; Matthews, J.; Nagata, A.; Niessen, S.; Ornelas, M.A.; Orr, S.T.M.; Pairish, M.; Planken, S.; Ren, S.; Richter, D.; Ryan, K.; Sach, N.; Shen, H.; Smeal, T.; Solowiej, J.; Sutton, S.; Tran, K.; Tseng, E.; Vernier, W.; Walls, M.; Wang, S.; Weinrich, S.L.; Xin, S.; Xu, H.; Yin, M.J.; Zientek, M.; Zhou, R.; Kath, J.C. Discovery of 1-(3 R, 4 R)-3-[(5-Chloro-2-[(1-methyl-1 H -pyrazol-4-yl)amino]-7 H -pyrrolo[2,3- d]pyrimidin-4-yloxy)methyl]-4-methoxypyrrolidin-1-ylprop-2-en-1-one (PF-06459988), a Potent, WT Sparing, Irreversible Inhibitor of T790M-Containing EGFR Mutants. J. Med. Chem., 2016, 59(5), 2005-2024.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01633] [PMID: 26756222]
[37]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[38]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[39]
Martínez-Rosell, G.; Giorgino, T.; De Fabritiis, G. Playmolecule proteinprepare: A web application for protein preparation for molecular dynamics simulations. J. Chem. Inf. Model., 2017, 57(7), 1511-1516.
[http://dx.doi.org/10.1021/acs.jcim.7b00190] [PMID: 28594549]
[40]
Dassault Systèmes, B.I.O.V.I.A. Discovery Studio Modeling Environment; Dassault Systèmes: San Diego, CA, USA, 2021.
[41]
Samdani, A.; Vetrivel, U. POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening. Comput. Biol. Chem., 2018, 74, 39-48.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.02.012] [PMID: 29533817]
[42]
Maestro: A Powerful, All-Purpose Molecular Modeling Environment. Available from: http://www.schrodinger.com/maestro
[43]
Elbadawi, M.M.; Eldehna, W.M.; Abd El-Hafeez, A.A.; Somaa, W.R.; Albohy, A.; Al-Rashood, S.T.; Agama, K.K.; Elkaeed, E.B.; Ghosh, P.; Pommier, Y.; Abe, M. 2-Arylquinolines as novel anticancer agents with dual EGFR/FAK kinase inhibitory activity: Synthesis, biological evaluation, and molecular modelling insights. J. Enzyme Inhib. Med. Chem., 2022, 37(1), 355-378.
[http://dx.doi.org/10.1080/14756366.2021.2015344] [PMID: 34923887]
[44]
Alam, M.M.; Nazreen, S.; Almalki, A.S.A.; Elhenawy, A.A.; Alsenani, N.I.; Elbehairi, S.E.I.; Malebari, A.M.; Alfaifi, M.Y.; Alsharif, M.A.; Alfaifi, S.Y.M. Naproxen based 1,3,4-oxadiazole derivatives as EGFR inhibitors: Design, synthesis, anticancer, and computational studies. Pharmaceuticals, 2021, 14(9), 870.
[http://dx.doi.org/10.3390/ph14090870] [PMID: 34577570]
[45]
Liu, L.T.; Yuan, T.T.; Liu, H.H.; Chen, S.F.; Wu, Y.T. Synthesis and biological evaluation of substituted 6-alkynyl-4-anilinoquinazoline derivatives as potent EGFR inhibitors. Bioorg. Med. Chem. Lett., 2007, 17(22), 6373-6377.
[http://dx.doi.org/10.1016/j.bmcl.2007.08.061] [PMID: 17889528]
[46]
Gramatica, P.; Chirico, N.; Papa, E.; Cassani, S.; Kovarich, S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. J. Comput. Chem., 2013, 34(24), 2121-2132.
[http://dx.doi.org/10.1002/jcc.23361]
[47]
Gramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem., 2014, 35(13), 1036-1044.
[http://dx.doi.org/10.1002/jcc.23576] [PMID: 24599647]
[48]
Gramatica, P. Principles of QSAR Modeling: Comments and suggestions from personal experience. Int. J. Quantita. Struct.-Prop. Relationships, 2020, 5(3)
[49]
Moriwaki, H.; Tian, Y.S.; Kawashita, N.; Takagi, T. Mordred: A molecular descriptor calculator. J. Cheminform., 2018, 10(1), 4.
[http://dx.doi.org/10.1186/s13321-018-0258-y] [PMID: 29411163]
[50]
Lin, S-K. Pharmacophore perception, development and use in drug design. Edited by Osman F. Güner. Molecules, 2000, 5(12), 987-989.
[http://dx.doi.org/10.3390/50700987]
[51]
Wermuth, C.G.; Ganellin, C.R.; Lindberg, P.; Mitscher, L.A. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl. Chem., 1998, 70(5), 1129-1143.
[http://dx.doi.org/10.1351/pac199870051129]
[52]
Schisterman, E.F.; Faraggi, D.; Reiser, B. Adjusting the generalized ROC curve for covariates. Stat. Med., 2004, 23(21), 3319-3331.
[http://dx.doi.org/10.1002/sim.1908] [PMID: 15490426]
[53]
Bamber, D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psychol., 1975, 12(4), 387-415.
[http://dx.doi.org/10.1016/0022-2496(75)90001-2]
[54]
Kirchmair, J.; Distinto, S.; Markt, P.; Schuster, D.; Spitzer, G.M.; Liedl, K.R.; Wolber, G. How to optimize shape-based virtual screening: Choosing the right query and including chemical information. J. Chem. Inf. Model., 2009, 49(3), 678-692.
[http://dx.doi.org/10.1021/ci8004226] [PMID: 19434901]
[55]
Drwal, M.N.; Banerjee, P.; Dunkel, M.; Wettig, M.R.; Preissner, R. ProTox: A web server for the In silico prediction of rodent oral toxicity. Nucleic Acids Res., 2014, 42(W1), W53-W58.
[http://dx.doi.org/10.1093/nar/gku401] [PMID: 24838562]
[56]
Ortiz, C.L.D.; Completo, G.C.; Nacario, R.C.; Nellas, R.B. Potential Inhibitors of Galactofuranosyltransferase 2 (GlfT2): Molecular Docking, 3D-QSAR, and In silico ADMETox Studies. Sci. Rep., 2019, 9(1), 17096.
[http://dx.doi.org/10.1038/s41598-019-52764-8] [PMID: 31745103]
[57]
Ongtanasup, T.; Mazumder, A.; Dwivedi, A.; Eawsakul, K. Homology modeling, molecular docking, molecular dynamic simulation, and drug-likeness of the modified alpha-mangostin against the β-tubulin protein of acanthamoeba keratitis. Molecules, 2022, 27(19), 6338.
[http://dx.doi.org/10.3390/molecules27196338] [PMID: 36234875]
[58]
Unni, P.A.; Lulu, S.S.; Pillai, G.G. Computational strategies towards developing novel antimelanogenic agents. Life Sci., 2020, 250117602
[http://dx.doi.org/10.1016/j.lfs.2020.117602]
[59]
Chandran, S. Machine Learning Model Deployment - A Simple Checklist. , 2021. Available from: https://towardsdatascience.com/machine-learningmodel-deployment-a-simplistic-checklist-dc5558a88d1b
[60]
Gramatica, P.; Sangion, A. A historical excursus on the statistical validation parameters for qsar models: A clarification concerning metrics and terminology. J. Chem. Inf. Model., 2016, 56(6), 1127-1131.
[http://dx.doi.org/10.1021/acs.jcim.6b00088] [PMID: 27218604]
[61]
Roy, K. On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opin. Drug Discov., 2007, 2(12), 1567-1577.
[http://dx.doi.org/10.1517/17460441.2.12.1567] [PMID: 23488901]
[62]
Garg, Rajni Smith, Carr Predicting the bioconcentration factor of highly hydrophobic organic chemicals. Food Chem. Toxicol., 2014, 69, 035.
[http://dx.doi.org/10.1016/j.fct.2014.03.035]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy