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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Review Article

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Author(s): Kuldeep Singh*, Bharat Bhushan and Bhoopendra Singh

Volume 20, Issue 5, 2024

Published on: 15 September, 2023

Page: [697 - 710] Pages: 14

DOI: 10.2174/1573409920666230914123005

Price: $65

Abstract

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Graphical Abstract

[1]
Sliwoski, G; Kothiwale, S; Meiler, J; Lowe, EW Computational methods in drug discovery. Pharmacol Rev, 2014, 66(1), 334.
[http://dx.doi.org/10.1124/pr.112.007336]
[2]
Doman, T.N.; McGovern, S.L.; Witherbee, B.J.; Kasten, T.P.; Kurumbail, R.; Stallings, W.C.; Connolly, D.T.; Shoichet, B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem., 2002, 45(11), 2213-2221.
[http://dx.doi.org/10.1021/jm010548w] [PMID: 12014959]
[3]
Adelusi, T.I.; Oyedele, A.Q.K.; Boyenle, I.D.; Ogunlana, A.T.; Adeyemi, R.O.; Ukachi, C.D.; Idris, M.O.; Olaoba, O.T.; Adedotun, I.O.; Kolawole, O.E.; Xiaoxing, Y.; Abdul-Hammed, M. Molecular modeling in drug discovery. Inform. Med. Unlocked,, 2022, 29, 100880.
[http://dx.doi.org/10.1016/j.imu.2022.100880]
[4]
Du Toit, A. Outbreak of a novel coronavirus. Nat. Rev. Microbiol., 2020, 18(3), 123-123.
[http://dx.doi.org/10.1038/s41579-020-0332-0] [PMID: 31988490]
[5]
Myers, S.; Baker, A. Drug discovery—an operating model for a new era. Nat. Biotechnol., 2001, 19(8), 727-730.
[http://dx.doi.org/10.1038/90765] [PMID: 11479559]
[6]
Manglik, A.; Lin, H.; Aryal, D.K.; McCorvy, J.D.; Dengler, D.; Corder, G.; Levit, A.; Kling, R.C.; Bernat, V.; Hübner, H.; Huang, X.P.; Sassano, M.F.; Giguère, P.M.; Löber, S.; Da Duan; Scherrer, G.; Kobilka, B.K.; Gmeiner, P.; Roth, B.L.; Shoichet, B.K. Structure-based discovery of opioid analgesics with reduced side effects. Nature, 2016, 537(7619), 185-190.
[http://dx.doi.org/10.1038/nature19112] [PMID: 27533032]
[7]
Porter, C.T.; Bartlett, G.J.; Thornton, J.M. The catalytic site atlas: A resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res., 2004, 32(90001), 129D-133.
[http://dx.doi.org/10.1093/nar/gkh028] [PMID: 14681376]
[8]
Arakaki, A.K.; Zhang, Y.; Skolnick, J. Large-scale assessment of the utility of low-resolution protein structures for biochemical function assignment. Bioinformatics, 2004, 20(7), 1087-1096.
[http://dx.doi.org/10.1093/bioinformatics/bth044] [PMID: 14764543]
[9]
Källberg, M.; Wang, H.; Wang, S.; Peng, J.; Wang, Z.; Lu, H.; Xu, J. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc., 2012, 7(8), 1511-1522.
[http://dx.doi.org/10.1038/nprot.2012.085] [PMID: 22814390]
[10]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[11]
Ejalonibu, M.A.; Ogundare, S.A.; Elrashedy, A.A.; Ejalonibu, M.A.; Lawal, M.M.; Mhlongo, N.N.; Kumalo, H.M. Drug discovery for Mycobacterium tuberculosis using structure-based computer- aided drug design approach. Int. J. Mol. Sci., 2021, 22(24), 13259.
[http://dx.doi.org/10.3390/ijms222413259] [PMID: 34948055]
[12]
Bassani, D.; Moro, S. Past, present, and future perspectives on computer-aided drug design methodologies. Molecules, 2023, 28(9), 3906.
[http://dx.doi.org/10.3390/molecules28093906] [PMID: 37175316]
[13]
Martin, L.; Hutchens, M.; Hawkins, C. Clinical trial cycle times continue to increase despite industry efforts. Nat. Rev. Drug Discov., 2017, 16(3), 157-157.
[http://dx.doi.org/10.1038/nrd.2017.21] [PMID: 28184041]
[14]
Mouchlis, V.D.; Afantitis, A.; Serra, A.; Fratello, M.; Papadiamantis, A.G.; Aidinis, V.; Lynch, I.; Greco, D.; Melagraki, G. Advances in de novo drug design: From conventional to machine learning methods. Int. J. Mol. Sci., 2021, 22(4), 1676.
[http://dx.doi.org/10.3390/ijms22041676] [PMID: 33562347]
[15]
Petrović, D.; Scott, J.S.; Bodnarchuk, M.S.; Lorthioir, O.; Boyd, S.; Hughes, G.M.; Lane, J.; Wu, A.; Hargreaves, D.; Robinson, J.; Sadowski, J. Virtual screening in the cloud identifies potent and selective ROS1 kinase inhibitors. J. Chem. Inf. Model., 2022, 62(16), 3832-3843.
[http://dx.doi.org/10.1021/acs.jcim.2c00644] [PMID: 35920716]
[16]
Gorgulla, C.; Boeszoermenyi, A.; Wang, Z.F.; Fischer, P.D.; Coote, P.W.; Padmanabha Das, K.M.; Malets, Y.S.; Radchenko, D.S.; Moroz, Y.S.; Scott, D.A.; Fackeldey, K.; Hoffmann, M.; Iavniuk, I.; Wagner, G.; Arthanari, H. An open-source drug discovery platform enables ultra-large virtual screens. Nature, 2020, 580(7805), 663-668.
[http://dx.doi.org/10.1038/s41586-020-2117-z] [PMID: 32152607]
[17]
Ooms, F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr. Med. Chem., 2000, 7(2), 141-158.
[http://dx.doi.org/10.2174/0929867003375317] [PMID: 10637360]
[18]
Guha, R. The ups and downs of structure–activity landscapes. In: Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology; Bajorath, J., Ed; Humana Press: Totowa, NJ, 2010; 672, pp. 101-117.
[http://dx.doi.org/10.1007/978-1-60761-839-3_3]
[19]
Fu, H.; Shao, X.; Cai, W. Computer-aided design of molecular machines: Techniques, paradigms and difficulties. Phys. Chem. Chem. Phys., 2022, 24(3), 1286-1299.
[http://dx.doi.org/10.1039/D1CP04942A] [PMID: 34951435]
[20]
Ferreira, L.; dos Santos, R.; Oliva, G.; Andricopulo, A. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384-13421.
[http://dx.doi.org/10.3390/molecules200713384] [PMID: 26205061]
[21]
Palazzesi, F.; Pozzan, A. Deep learning applied to ligand-based de novo drug design. In: Artificial Intelligence in Drug Design. Methods in Molecular Biology; Humana: New York, NY, 2022; vol 2390, pp. 273-299.
[http://dx.doi.org/10.1007/978-1-0716-1787-8_12]
[22]
Yu, W; Mackerell, AD. Computer-aided drug design methods., Methods Mol Biol, 2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5]
[23]
Zhang, Y.; Luo, M.; Wu, P.; Wu, S.; Lee, T.Y.; Bai, C. Application of computational biology and artificial intelligence in drug design. Int. J. Mol. Sci., 2022, 23(21), 13568.
[http://dx.doi.org/10.3390/ijms232113568] [PMID: 36362355]
[24]
Pecina, A.; Eyrilmez, S.M.; Köprülüoğlu, C.; Miriyala, V.M.; Lepšík, M.; Fanfrlík, J.; Řezáč, J.; Hobza, P. SQM/COSMO scoring function: Reliable quantum‐mechanical tool for sampling and ranking in structure‐based drug design. ChemPlusChem, 2020, 85(11), 2362-2371.
[http://dx.doi.org/10.1002/cplu.202000120] [PMID: 32609421]
[25]
Monteleone, S.; Fedorov, D.G.; Townsend-Nicholson, A.; Southey, M.; Bodkin, M.; Heifetz, A. Hotspot identification and drug design of protein–protein interaction modulators using the fragment molecular orbital method. J. Chem. Inf. Model., 2022, 62(16), 3784-3799.
[http://dx.doi.org/10.1021/acs.jcim.2c00457] [PMID: 35939049]
[26]
Tripathi, A.; Bankaitis, VA. Molecular docking: From lock and key to combination lock. J. Mol. Med. Clin. Appl., 2017, 2(1), 10.
[27]
Maiti, S.; Nazmeen, A.; Banerjee, A. Significant impact of redox regulation of estrogen‐metabolizing proteins on cellular stress responses. Cell Biochem. Funct., 2023, 41(4), 461-477.
[http://dx.doi.org/10.1002/cbf.3796] [PMID: 37139830]
[28]
Zhou, S.; Weiß, R.G.; Cheng, L.T.; Dzubiella, J.; McCammon, J.A.; Li, B. Variational implicit-solvent predictions of the dry–wet transition pathways for ligand–receptor binding and unbinding kinetics. Proc. Natl. Acad. Sci. USA, 2019, 116(30), 14989-14994.
[http://dx.doi.org/10.1073/pnas.1902719116] [PMID: 31270236]
[29]
Śledź, P.; Caflisch, A. Protein structure-based drug design: From docking to molecular dynamics. Curr. Opin. Struct. Biol., 2018, 48, 93-102.
[http://dx.doi.org/10.1016/j.sbi.2017.10.010] [PMID: 29149726]
[30]
Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Eastwood, M.P.; Dror, R.O.; Shaw, D.E. Systematic validation of protein force fields against experimental data. PLoS One, 2012, 7(2), e32131.
[http://dx.doi.org/10.1371/journal.pone.0032131] [PMID: 22384157]
[31]
Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M.K.; Greenwood, J.; Romero, D.L.; Masse, C.; Knight, J.L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D.L.; Jorgensen, W.L.; Berne, B.J.; Friesner, R.A.; Abel, R. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc., 2015, 137(7), 2695-2703.
[http://dx.doi.org/10.1021/ja512751q] [PMID: 25625324]
[32]
Caleman, C.; van Maaren, P.J.; Hong, M.; Hub, J.S.; Costa, L.T.; van der Spoel, D. Force field benchmark of organic liquids: Density, enthalpy of vaporization, heat capacities, surface tension, isothermal compressibility, volumetric expansion coefficient, and dielectric constant. J. Chem. Theory Comput., 2012, 8(1), 61-74.
[http://dx.doi.org/10.1021/ct200731v] [PMID: 22241968]
[33]
Karunakar, P.; P B, S.; v, K. In silico modelling and virtual screening for identification of inhibitors for spore wall protein-5 in Nosema bombycis. J. Biomol. Struct. Dyn., 2022, 40(4), 1748-1763.
[http://dx.doi.org/10.1080/07391102.2020.1832579] [PMID: 33050775]
[34]
Hassan Baig, M.; Ahmad, K.; Roy, S.; Mohammad Ashraf, J.; Adil, M.; Haris Siddiqui, M.; Khan, S.; Amjad Kamal, M.; Provazník, I.; Choi, I. Computer aided drug design: Success and limitations. Curr. Pharm. Des., 2016, 22(5), 572-581.
[http://dx.doi.org/10.2174/1381612822666151125000550] [PMID: 26601966]
[35]
Seidel, T.; Schuetz, D.A.; Garon, A.; Langer, T. The pharmacophore concept and its applications in computer-aided drug design. In: Progress in the Chemistry of Organic Natural Products; Kinghorn, A.; Falk, H.; Gibbons, S., Eds.; Springer: Cham, 2019; 110, pp. 99-141.
[http://dx.doi.org/10.1007/978-3-030-14632-0_4]
[36]
Yang, D.; Zhou, Q.; Labroska, V.; Qin, S.; Darbalaei, S.; Wu, Y.; Yuliantie, E.; Xie, L.; Tao, H.; Cheng, J.; Liu, Q.; Zhao, S.; Shui, W.; Jiang, Y.; Wang, M.W. G protein-coupled receptors: Structureand function-based drug discovery. Signal Transduct. Target. Ther., 2021, 6(1), 7.
[http://dx.doi.org/10.1038/s41392-020-00435-w] [PMID: 33414387]
[37]
Morris, G.M.; Lim-Wilby, M. Molecular docking. Methods Mol Biol., 2008, 443, 365-382.
[http://dx.doi.org/10.1007/978-1-59745-177-2_19.] [PMID: 18446297]
[38]
Shahin, R.; Mansi, I.; Swellmeen, L.; Alwidyan, T.; Al-Hashimi, N.; Al-Qarar’h, Y.; Shaheen, O. Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors. J. Mol. Graph. Model., 2018, 80, 327-352.
[http://dx.doi.org/10.1016/j.jmgm.2018.01.004] [PMID: 29454290]
[39]
Ballante, F.; Kooistra, A.J.; Kampen, S.; de Graaf, C.; Carlsson, J. Structure-based virtual screening for ligands of G protein-coupled receptors: What can molecular docking do for you? Pharmacol Rev, 2021, 73(4), 527-565.
[40]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[41]
Blanes-Mira, C.; Fernández-Aguado, P.; de Andrés-López, J.; Fernández-Carvajal, A.; Ferrer-Montiel, A.; Fernández-Ballester, G. Comprehensive survey of consensus docking for highthroughput virtual screening. Molecules, 2022, 28(1), 175.
[http://dx.doi.org/10.3390/molecules28010175] [PMID: 36615367]
[42]
Tahir ul Qamar, M.; Zhu, XT.; Chen, LL; Alhussain, L Targetspecific machine learning scoring function improved structurebased virtual screening performance for SARS-CoV-2 drugs development. Int J Mol Sci, 2022, 23(19), 11003.
[43]
Wu, C; Liu, Y; Yang, Y; Zhang, P; Zhong, W; Wang, Y Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B, 2020, 10(5), 766.
[44]
Rajasekaran, R.; Chen, Y.P.P. Probing the structure of Leishmania major DHFR TS and structure based virtual screening of peptide library for the identification of anti-leishmanial leads. J. Mol. Model., 2012, 18(9), 4089-4100.
[http://dx.doi.org/10.1007/s00894-012-1411-6] [PMID: 22527276]
[45]
Editorial (Hot Topic: Topological and electrotopological descriptors of molecules: Fundamental principles and applications to computer aided molecular design – Part II). Curr Comput Aided- Drug Des., 2012, 8(3), 171.
[http://dx.doi.org/10.2174/157340912801619111]
[46]
Llinas del Torrent, C.; Pérez-Benito, L.; Tresadern, G. Computational drug design applied to the study of metabotropic glutamate receptors. Molecules, 2019, 24(6), 1098.
[http://dx.doi.org/10.3390/molecules24061098] [PMID: 30897742]
[47]
Barril, X.; Hubbard, R.E.; Morley, S.D. Virtual screening in structure- based drug discovery. Mini Rev. Med. Chem., 2004, 4(7), 779-791.
[PMID: 15379645]
[48]
Gao, Y.; Zhou, Z.; Zhang, T.; Xue, S.; Li, K.; Jiang, J. Structurebased virtual screening towards the discovery of novel ULK1 inhibitors with anti-HCC activities. Molecules, 2022, 27(9), 2627.
[http://dx.doi.org/10.3390/molecules27092627] [PMID: 35565977]
[49]
Dong, J.; Cao, D.S.; Miao, H.Y.; Liu, S.; Deng, B.C.; Yun, Y.H.; Wang, N.N.; Lu, A.P.; Zeng, W.B.; Chen, A.F. ChemDes: An integrated web-based platform for molecular descriptor and fingerprint computation. J. Cheminform., 2015, 7(1), 60.
[http://dx.doi.org/10.1186/s13321-015-0109-z] [PMID: 26664458]
[50]
Vucicevic, J.; Nikolic, K.; Mitchell, J.B.O. Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches. Curr. Med. Chem., 2019, 26(21), 3874-3889.
[http://dx.doi.org/10.2174/0929867324666170712115411] [PMID: 28707592]
[51]
Huynh, L.; Neale, C.; Pomès, R.; Allen, C. Computational approaches to the rational design of nanoemulsions, polymeric micelles, and dendrimers for drug delivery. Nanomedicine, 2012, 8(1), 20-36.
[http://dx.doi.org/10.1016/j.nano.2011.05.006] [PMID: 21669300]
[52]
Zhang, S.; Zhang, J.; Gao, P.; Sun, L.; Song, Y.; Kang, D.; Liu, X.; Zhan, P. Efficient drug discovery by rational lead hybridization based on crystallographic overlay. Drug Discov. Today, 2019, 24(3), 805-813.
[http://dx.doi.org/10.1016/j.drudis.2018.11.021] [PMID: 30529326]
[53]
Testa, A.; Hughes, S.J.; Lucas, X.; Wright, J.E.; Ciulli, A. Structure‐ based design of a macrocyclic PROTAC. Angew. Chem. Int. Ed., 2020, 59(4), 1727-1734.
[http://dx.doi.org/10.1002/anie.201914396] [PMID: 31746102]
[54]
Krüger, D.M.; Evers, A. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem, 2010, 5(1), 148-158.
[http://dx.doi.org/10.1002/cmdc.200900314] [PMID: 19908272]
[55]
Rush, T.S., III; Grant, J.A.; Mosyak, L.; Nicholls, A. A shapebased 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J. Med. Chem., 2005, 48(5), 1489-1495.
[http://dx.doi.org/10.1021/jm040163o] [PMID: 15743191]
[56]
Razzaghi-Asl, N.; Sepehri, S.; Ebadi, A.; Miri, R.; Shahabipour, S. Effect of biomolecular conformation on docking simulation: A case study on a potent HIV-1 protease inhibitor. Iran. J. Pharm. Res., 2015, 14(3), 785-802.
[PMID: 26330867]
[57]
Lin, J.; Sahakian, D.; de Morais, S.; Xu, J.; Polzer, R.; Winter, S. The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery. Curr. Top. Med. Chem., 2003, 3(10), 1125-1154.
[http://dx.doi.org/10.2174/1568026033452096] [PMID: 12769713]
[58]
Ansari, S.; Azizian, H.; Pedrood, K.; Yavari, A.; Mojtabavi, S.; Faramarzi, M.A.; Golshani, S.; Hosseini, S.; Biglar, M.; Larijani, B.; Rastegar, H.; Hamedifar, H.; Mohammadi-Khanaposhtani, M.; Mahdavi, M. Design, synthesis, and α‐glucosidase‐inhibitory activity of phenoxy‐biscoumarin –N ‐phenylacetamide hybrids. Arch. Pharm., 2021, 354(12), 2100179.
[http://dx.doi.org/10.1002/ardp.202100179] [PMID: 34467580]
[59]
Zhu, Y.; Han, Y.; Ma, Y.; Yang, P. ADME/toxicity prediction and antitumor activity of novel nitrogenous heterocyclic compounds designed by computer targeting of alkylglycerone phosphate synthase. Oncol. Lett., 2018, 16(2), 1431-1438.
[http://dx.doi.org/10.3892/ol.2018.8873] [PMID: 30008821]
[60]
Rai, H.; Barik, A.; Singh, Y.P.; Suresh, A.; Singh, L.; Singh, G.; Nayak, U.Y.; Dubey, V.K.; Modi, G. Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/ toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19. Mol. Divers., 2021, 25(3), 1905-1927.
[http://dx.doi.org/10.1007/s11030-021-10188-5] [PMID: 33582935]
[61]
Parmar, D.R.; Soni, J.Y.; Guduru, R.; Rayani, R.H.; Kusurkar, R.V.; Vala, A.G.; Talukdar, S.N.; Eissa, I.H.; Metwaly, A.M.; Khalil, A.; Zunjar, V.; Battula, S. Discovery of new anticancer thiourea- azetidine hybrids: design, synthesis, in vitro antiproliferative, SAR, in silico molecular docking against VEGFR-2, ADMET, toxicity, and DFT studies. Bioorg. Chem., 2021, 115, 105206.
[http://dx.doi.org/10.1016/j.bioorg.2021.105206] [PMID: 34339975]
[62]
McKim, J., Jr Building a tiered approach to in vitro predictive toxicity screening: A focus on assays with in vivo relevance. Comb. Chem. High Throughput Screen., 2010, 13(2), 188-206.
[http://dx.doi.org/10.2174/138620710790596736] [PMID: 20053163]
[63]
Alanazi, M.M.; Elwan, A.; Alsaif, N.A.; Obaidullah, A.J.; Alkahtani, H.M.; Al-Mehizia, A.A.; Alsubaie, S.M.; Taghour, M.S.; Eissa, I.H. Discovery of new 3-methylquinoxalines as potential anti- cancer agents and apoptosis inducers targeting VEGFR-2: design, synthesis, and in silico studies. J. Enzyme Inhib. Med. Chem., 2021, 36(1), 1732-1750.
[http://dx.doi.org/10.1080/14756366.2021.1945591] [PMID: 34325596]
[64]
Idris, M.O.; Yekeen, A.A.; Alakanse, O.S.; Durojaye, O.A. Computer- aided screening for potential TMPRSS2 inhibitors: A combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches. J. Biomol. Struct. Dyn., 2021, 39(15), 5638-5656.
[http://dx.doi.org/10.1080/07391102.2020.1792346] [PMID: 32672528]
[65]
Daoui, O.; Nour, H.; Abchir, O.; Elkhattabi, S.; Bakhouch, M.; Chtita, S. A computer-aided drug design approach to explore novel type II inhibitors of c-Met receptor tyrosine kinase for cancer therapy: QSAR, molecular docking, ADMET and molecular dynamics simulations. J. Biomol. Struct. Dyn., 2023, 41(16), 7768-7785.
[http://dx.doi.org/10.1080/07391102.2022.2124456] [PMID: 36120976]
[66]
Tabeshpour, J.; Sahebkar, A.; Zirak, M.R.; Zeinali, M.; Hashemzaei, M.; Rakhshani, S.; Rakhshani, S. Computer-aided drug design and drug pharmacokinetic prediction: A mini-review. Curr. Pharm. Des., 2018, 24(26), 3014-3019.
[http://dx.doi.org/10.2174/1381612824666180903123423] [PMID: 30179125]
[67]
Sodum, N; Rao, V; Cheruku, SP; Kumar, G; Sankhe, R; Kishore, A. Amelioration of high-fat diet (HFD) + CCl4 induced NASH/NAFLD in CF-1 mice by activation of SIRT-1 using cinnamoyl sulfonamide hydroxamate derivatives: In-silico molecular modelling and in-vivo prediction. 3 Biotech, 2022, 12(7), 147.
[68]
Rim, K.T. In silico prediction of toxicity and its applications for chemicals at work. Toxicol. Environ. Health Sci., 2020, 12(3), 191-202.
[http://dx.doi.org/10.1007/s13530-020-00056-4] [PMID: 32421081]
[69]
Tripathy, S.; Sahu, S.K.; Azam, M.A.; Jupudi, S. Computer-aided identification of lead compounds as Staphylococcal epidermidis FtsZ inhibitors using molecular docking, virtual screening, DFT analysis, and molecular dynamic simulation. J. Mol. Model., 2019, 25(12), 360.
[http://dx.doi.org/10.1007/s00894-019-4238-6] [PMID: 31773394]
[70]
Krishnan, S.R.; Bung, N.; Vangala, S.R.; Srinivasan, R.; Bulusu, G.; Roy, A. De novo structure-based drug design using deep learning. J. Chem. Inf. Model., 2022, 62(21), 5100-5109.
[http://dx.doi.org/10.1021/acs.jcim.1c01319] [PMID: 34792338]
[71]
Lin, Y.; Zhang, Y.; Wang, D.; Yang, B.; Shen, Y.Q. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. Phytomedicine, 2022, 107, 154481.
[http://dx.doi.org/10.1016/j.phymed.2022.154481] [PMID: 36215788]
[72]
Congreve, M.; Murray, C.W.; Blundell, T.L. Keynote review: Structural biology and drug discovery. Drug Discov. Today, 2005, 10(13), 895-907.
[http://dx.doi.org/10.1016/S1359-6446(05)03484-7] [PMID: 15993809]
[73]
Hartenfeller, M.; Schneider, G. De novo drug design. In: Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology; Bajorath, J., Ed.; Humana Press: Totowa, N, 2010; 672, pp. 299-332.
[http://dx.doi.org/10.1007/978-1-60761-839-3_12]
[74]
Papadopoulos, K.; Giblin, K.A.; Janet, J.P.; Patronov, A.; Engkvist, O. De novo design with deep generative models based on 3D similarity scoring. Bioorg. Med. Chem., 2021, 44, 116308.
[http://dx.doi.org/10.1016/j.bmc.2021.116308] [PMID: 34280849]
[75]
Shulga, D.A.; Ivanov, N.N.; Palyulin, V.A. In silico structure-based approach for group efficiency estimation in fragment-based drug design using evaluation of fragment contributions. Molecules, 2022, 27(6), 1985.
[http://dx.doi.org/10.3390/molecules27061985] [PMID: 35335347]
[76]
Ullah, A.; Khan, A.; Al-Harrasi, A.; Ullah, K.; Shabbir, A. Threedimensional structure characterization and inhibition study of exfoliative toxin D from staphylococcus aureus. Front. Pharmacol., 2022, 13(Feb), 800970.
[http://dx.doi.org/10.3389/fphar.2022.800970] [PMID: 35250557]
[77]
Wu, K.; Bai, H.; Chang, Y.T.; Redler, R.; McNally, K.E.; Sheffler, W.; Brunette, T.J.; Hicks, D.R.; Morgan, T.E.; Stevens, T.J.; Broerman, A.; Goreshnik, I.; DeWitt, M.; Chow, C.M.; Shen, Y.; Stewart, L.; Derivery, E.; Silva, D.A.; Bhabha, G.; Ekiert, D.C.; Baker, D. De novo design of modular peptide-binding proteins by superhelical matching. Nature, 2023, 616(7957), 581-589.
[http://dx.doi.org/10.1038/s41586-023-05909-9] [PMID: 37020023]
[78]
Smith, M.D.; Rao, J.S.; Segelken, E.; Cruz, L. Force-field induced bias in the structure of Aβ 21–30 : A comparison of OPLS, AMBER, CHARMM, and GROMOS force fields. J. Chem. Inf. Model., 2015, 55(12), 2587-2595.
[http://dx.doi.org/10.1021/acs.jcim.5b00308] [PMID: 26629886]
[79]
Kiss, G.; Röthlisberger, D.; Baker, D.; Houk, K.N. Evaluation and ranking of enzyme designs. Protein Sci., 2010, 19(9), 1760-1773.
[http://dx.doi.org/10.1002/pro.462] [PMID: 20665693]
[80]
Krieger, E.; Joo, K.; Lee, J.; Lee, J.; Raman, S.; Thompson, J.; Tyka, M.; Baker, D.; Karplus, K. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins, 2009, 77(S9)(Suppl. 9), 114-122.
[http://dx.doi.org/10.1002/prot.22570] [PMID: 19768677]
[81]
Batool, M.; Ahmad, B.; Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci., 2019, 20(11), 2783.
[http://dx.doi.org/10.3390/ijms20112783] [PMID: 31174387]
[82]
Emilien, G.; Ponchon, M.; Caldas, C.; Isacson, O.; Maloteaux, J.M. Impact of genomics on drug discovery and clinical medicine. QJM, 2000, 93(7), 391-423.
[http://dx.doi.org/10.1093/qjmed/93.7.391] [PMID: 10874050]
[83]
da Silva Rocha, S.F.L.; Olanda, C.G.; Fokoue, H.H.; Sant’Anna, C.M.R. Virtual screening techniques in drug discovery: Review and recent applications. Curr. Top. Med. Chem., 2019, 19(19), 1751-1767.
[http://dx.doi.org/10.2174/1568026619666190816101948] [PMID: 31418662]
[84]
Trott, O; Olson, AJ AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.. J Comput Chem, 2009, 31(2), 455-461.
[85]
Hosseini, M.; Chen, W.; Xiao, D.; Wang, C. Computational molecular docking and virtual screening revealed promising SARS-CoV- 2 drugs. Precis. Clin. Med., 2021, 4(1), 1-16.
[http://dx.doi.org/10.1093/pcmedi/pbab001] [PMID: 33842834]
[86]
Chen, Z.; Li, H.; Zhang, Q.; Bao, X.; Yu, K.; Luo, X.; Zhu, W.; Jiang, H. Pharmacophore-based virtual screening versus dockingbased virtual screening: a benchmark comparison against eight targets. Acta Pharmacol. Sin., 2009, 30(12), 1694-1708.
[http://dx.doi.org/10.1038/aps.2009.159] [PMID: 19935678]
[87]
Giordano, D.; Biancaniello, C.; Argenio, M.A.; Facchiano, A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals, 2022, 15(5), 646.
[http://dx.doi.org/10.3390/ph15050646] [PMID: 35631472]
[88]
Van Drie, J.H. Computer-aided drug design: the next 20 years. J. Comput. Aided Mol. Des., 2007, 21(10-11), 591-601.
[http://dx.doi.org/10.1007/s10822-007-9142-y] [PMID: 17989929]
[89]
León, R.; Soto-Delgado, J.; Montero, E.; Vargas, M. Development of computational approaches with a fragment-based drug design strategy: In silico hsp90 inhibitors discovery. Int. J. Mol. Sci., 2021, 22(24), 13226.
[http://dx.doi.org/10.3390/ijms222413226] [PMID: 34948022]
[90]
Michel, M.; Homan, E.J.; Wiita, E.; Pedersen, K.; Almlöf, I.; Gustavsson, A.L.; Lundbäck, T.; Helleday, T.; Warpman Berglund, U. In silico druggability assessment of the NUDIX hydrolase protein family as a workflow for target prioritization. Front Chem., 2020, 8, 443.
[http://dx.doi.org/10.3389/fchem.2020.00443] [PMID: 32548091]
[91]
Jäntschi, L Prediction of physico-chemical and biological properties with the help of mathematical descriptors; Ian: Cluj-Napoca, 2000.
[92]
Jäntschi, L. Molecular descriptors family on structure activity relationships 1. Review of the methodology. Leonardo J. Pract. Technol., 2005, 4(6), 76-98.
[93]
Bolboacă, S.D.; Jäntschi, L. Comparison of QSAR performances on carboquinone derivatives. ScientificWorldJournal, 2009, 9(10), 1148-1166.
[http://dx.doi.org/10.1100/tsw.2009.131] [PMID: 19838601]
[94]
Bolboacă, S.D.; Jäntschi, L. Nano-quantitative structure-property relationship modeling on C42 fullerene isomers. J. Chem., 2016, 2016, 1-8.
[http://dx.doi.org/10.1155/2016/1791756]
[95]
Wang, W.; He, S.; Dong, G.; Sheng, C. Nucleic-acid-based targeted degradation in drug discovery. J. Med. Chem., 2022, 65(15), 10217-10232.
[http://dx.doi.org/10.1021/acs.jmedchem.2c00875] [PMID: 35916496]
[96]
Diller, D.J.; Swanson, J.; Bayden, A.S.; Jarosinski, M.; Audie, J. Rational, computer-enabled peptide drug design: Principles, methods, applications and future directions. Future Med. Chem., 2015, 7(16), 2173-2193.
[http://dx.doi.org/10.4155/fmc.15.142] [PMID: 26510691]
[97]
Dong, D.; Xu, Z.; Zhong, W.; Peng, S. Parallelization of molecular docking: A review. Curr. Top. Med. Chem., 2018, 18(12), 1015-1028.
[http://dx.doi.org/10.2174/1568026618666180821145215] [PMID: 30129415]
[98]
Wadood, A.; Ghufran, M.; Hassan, S.F.; Khan, H.; Azam, S.S.; Rashid, U. In silico identification of promiscuous scaffolds as potential inhibitors of 1-deoxy- D -xylulose 5-phosphate reductoisomerase for treatment of Falciparum malaria. Pharm. Biol., 2017, 55(1), 19-32.
[http://dx.doi.org/10.1080/13880209.2016.1225778] [PMID: 27650666]
[99]
Liu, T.; Lu, D.; Zhang, H.; Zheng, M.; Yang, H.; Xu, Y.; Luo, C.; Zhu, W.; Yu, K.; Jiang, H. Applying high-performance computing in drug discovery and molecular simulation. Natl. Sci. Rev., 2016, 3(1), 49-63.
[http://dx.doi.org/10.1093/nsr/nww003] [PMID: 32288960]
[100]
Pérez, B.; Antunes, S.; Gonçalves, L.M.; Domingos, A.; Gomes, J.R.B.; Gomes, P.; Teixeira, C. Toward the discovery of inhibitors of babesipain-1, a Babesia bigemina cysteine protease: in vitro evaluation, homology modeling and molecular docking studies. J. Comput. Aided Mol. Des., 2013, 27(9), 823-835.
[http://dx.doi.org/10.1007/s10822-013-9682-2] [PMID: 24129820]
[101]
Jiang, Y.; Yang, M.; Wang, S.; Li, X.; Sun, Y. Emerging role of deep learning‐based artificial intelligence in tumor pathology. Cancer Commun., 2020, 40(4), 154-166.
[http://dx.doi.org/10.1002/cac2.12012] [PMID: 32277744]
[102]
Huang, W.; Zhang, L.; Li, Z. Advances in computer-aided drug design for type 2 diabetes. Expert Opin. Drug Discov., 2022, 17(5), 461-472.
[http://dx.doi.org/10.1080/17460441.2022.2047644] [PMID: 35254188]
[103]
Usha, T.; Shanmugarajan, D.; Goyal, A.K.; Kumar, C.S.; Middha, S.K. Recent updates on computer-aided drug discovery: Time for a paradigm shift. Curr. Top. Med. Chem., 2018, 17(30), 3296-3307.
[http://dx.doi.org/10.2174/1568026618666180101163651] [PMID: 29295698]
[104]
Gurung, A.B.; Ali, M.A.; Lee, J.; Farah, M.A.; Al-Anazi, K.M. An updated review of computer-aided drug design and its application to COVID-19. Biomed Res Int, 2021, 2021, 8853056.
[105]
Wang, L.; Sarafianos, S.G.; Wang, Z. Cutting into the substrate dominance: Pharmacophore and structure-based approaches toward inhibiting human immunodeficiency virus reverse transcriptaseassociated ribonuclease H. Acc. Chem. Res., 2020, 53(1), 218-230.
[http://dx.doi.org/10.1021/acs.accounts.9b00450] [PMID: 31880912]
[106]
Zeb, A.; Park, C.; Rampogu, S.; Son, M.; Lee, G.; Lee, K.W. Structure- based drug designing recommends HDAC6 inhibitors to attenuate microtubule-associated tau-pathogenesis. ACS Chem. Neurosci., 2019, 10(3), 1326-1335.
[http://dx.doi.org/10.1021/acschemneuro.8b00405] [PMID: 30407786]
[107]
Simon, L.; Imane, A.; Srinivasan, K.K.; Pathak, L.; Daoud, I. In silico drug-designing studies on flavanoids as anticolon cancer agents: Pharmacophore mapping, molecular docking, and monte carlo method-based QSAR modeling. Interdiscip. Sci., 2017, 9(3), 445-458.
[http://dx.doi.org/10.1007/s12539-016-0169-4] [PMID: 27059855]
[108]
Shen, L.; Huang, H.; Makriyannis, A.; Fisher, L.S. Integrated ligand based pharmacophore model derived from diverse FAAH covalent ligand classes. Curr. Computeraided Drug Des., 2012, 8(4), 330-334.
[http://dx.doi.org/10.2174/157340912803519615] [PMID: 22734710]
[109]
Kale, A.; Kakde, R.; Pawar, S.; Jagtap, V.; Dorugade, R. Importance of pharmacophore in designing anticonvulsant agents. CNS Neurol. Disord. Drug Targets, 2023, 22(4), 500-511.
[http://dx.doi.org/10.2174/1871527321666220401115529] [PMID: 35366788]
[110]
Kesharwani, R.K.; Singh, D.V.; Misra, K. Computation-based virtual screening for designing novel antimalarial drugs by targeting falcipain-III: a structure-based drug designing approach. J. Vector Borne Dis., 2013, 50(2), 93-102.
[PMID: 23995310]

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