Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64427710.2174/1573409920666230914123005ChemistryResearch ArticleAdvances in Drug Discovery and Design using Computer-aided Molecular ModelingSinghKuldeepinfo@benthamscience.netBhushanBharatinfo@benthamscience.netSinghBhoopendrainfo@benthamscience.netDepartment of Pharmacology, Rajiv Academy for PharmacyDepartment of Pharmacology, Institute of Pharmaceutical Research, GLA UniversityDepartment of Pharmacy,, B.S.A. College of Engineering & Technology0105202420569771007012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/644277

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.

Computer-aided molecular modelingvirtual screeningquantitative structure-activity relationship (QSAR)molecular dynamics simulationdrug design optimizationpharmacophore modeling.Sliwoski, G; Kothiwale, S; Meiler, J; Lowe, EW Computational methods in drug discovery. Pharmacol Rev, 2014, 66(1), 334. doi: 10.1124/pr.112.007336Doman, 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. doi: 10.1021/jm010548w PMID: 12014959Adelusi, 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. doi: 10.1016/j.imu.2022.100880Du Toit, A. Outbreak of a novel coronavirus. Nat. Rev. Microbiol., 2020, 18(3), 123-123. doi: 10.1038/s41579-020-0332-0 PMID: 31988490Myers, S.; Baker, A. Drug discovery—an operating model for a new era. Nat. Biotechnol., 2001, 19(8), 727-730. doi: 10.1038/90765 PMID: 11479559Manglik, 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. doi: 10.1038/nature19112 PMID: 27533032Porter, 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. doi: 10.1093/nar/gkh028 PMID: 14681376Arakaki, 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. doi: 10.1093/bioinformatics/bth044 PMID: 14764543Källberg, 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. doi: 10.1038/nprot.2012.085 PMID: 22814390Gupta, 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. doi: 10.1007/s11030-021-10217-3 PMID: 33844136Ejalonibu, 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. doi: 10.3390/ijms222413259 PMID: 34948055Bassani, D.; Moro, S. Past, present, and future perspectives on computer-aided drug design methodologies. Molecules, 2023, 28(9), 3906. doi: 10.3390/molecules28093906 PMID: 37175316Martin, L.; Hutchens, M.; Hawkins, C. Clinical trial cycle times continue to increase despite industry efforts. Nat. Rev. Drug Discov., 2017, 16(3), 157-157. doi: 10.1038/nrd.2017.21 PMID: 28184041Mouchlis, 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. doi: 10.3390/ijms22041676 PMID: 33562347Petrović, 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. doi: 10.1021/acs.jcim.2c00644 PMID: 35920716Gorgulla, 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. doi: 10.1038/s41586-020-2117-z PMID: 32152607Ooms, F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr. Med. Chem., 2000, 7(2), 141-158. doi: 10.2174/0929867003375317 PMID: 10637360Guha, 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. doi: 10.1007/978-1-60761-839-3_3Fu, H.; Shao, X.; Cai, W. Computer-aided design of molecular machines: Techniques, paradigms and difficulties. Phys. Chem. Chem. Phys., 2022, 24(3), 1286-1299. doi: 10.1039/D1CP04942A PMID: 34951435Ferreira, L.; dos Santos, R.; Oliva, G.; Andricopulo, A. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384-13421. doi: 10.3390/molecules200713384 PMID: 26205061Palazzesi, 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. doi: 10.1007/978-1-0716-1787-8_12Yu, W; Mackerell, AD. Computer-aided drug design methods., Methods Mol Biol, 2017, 1520, 85-106. doi: 10.1007/978-1-4939-6634-9_5Zhang, 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. doi: 10.3390/ijms232113568 PMID: 36362355Pecina, 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. doi: 10.1002/cplu.202000120 PMID: 32609421Monteleone, 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. doi: 10.1021/acs.jcim.2c00457 PMID: 35939049Tripathi, A.; Bankaitis, VA. Molecular docking: From lock and key to combination lock. J. Mol. Med. Clin. Appl., 2017, 2(1), 10.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. doi: 10.1002/cbf.3796 PMID: 37139830Zhou, 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. doi: 10.1073/pnas.1902719116 PMID: 31270236Śledź, P.; Caflisch, A. Protein structure-based drug design: From docking to molecular dynamics. Curr. Opin. Struct. Biol., 2018, 48, 93-102. doi: 10.1016/j.sbi.2017.10.010 PMID: 29149726Lindorff-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. doi: 10.1371/journal.pone.0032131 PMID: 22384157Wang, 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. doi: 10.1021/ja512751q PMID: 25625324Caleman, 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. doi: 10.1021/ct200731v PMID: 22241968Karunakar, 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. doi: 10.1080/07391102.2020.1832579 PMID: 33050775Hassan 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. doi: 10.2174/1381612822666151125000550 PMID: 26601966Seidel, 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. doi: 10.1007/978-3-030-14632-0_4Yang, 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. doi: 10.1038/s41392-020-00435-w PMID: 33414387Morris, G.M.; Lim-Wilby, M. Molecular docking. Methods Mol Biol., 2008, 443, 365-382. doi: 10.1007/978-1-59745-177-2_19. PMID: 18446297Shahin, 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. doi: 10.1016/j.jmgm.2018.01.004 PMID: 29454290Ballante, 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.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. doi: 10.1038/nprot.2016.051 PMID: 27077332Blanes-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. doi: 10.3390/molecules28010175 PMID: 36615367Tahir 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.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.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. doi: 10.1007/s00894-012-1411-6 PMID: 22527276Editorial (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. doi: 10.2174/157340912801619111Llinas del Torrent, C.; Pérez-Benito, L.; Tresadern, G. Computational drug design applied to the study of metabotropic glutamate receptors. Molecules, 2019, 24(6), 1098. doi: 10.3390/molecules24061098 PMID: 30897742Barril, X.; Hubbard, R.E.; Morley, S.D. Virtual screening in structure- based drug discovery. Mini Rev. Med. Chem., 2004, 4(7), 779-791. PMID: 15379645Gao, 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. doi: 10.3390/molecules27092627 PMID: 35565977Dong, 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. doi: 10.1186/s13321-015-0109-z PMID: 26664458Vucicevic, 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. doi: 10.2174/0929867324666170712115411 PMID: 28707592Huynh, 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. doi: 10.1016/j.nano.2011.05.006 PMID: 21669300Zhang, 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. doi: 10.1016/j.drudis.2018.11.021 PMID: 30529326Testa, 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. doi: 10.1002/anie.201914396 PMID: 31746102Krüger, 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. doi: 10.1002/cmdc.200900314 PMID: 19908272Rush, 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. doi: 10.1021/jm040163o PMID: 15743191Razzaghi-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: 26330867Lin, 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. doi: 10.2174/1568026033452096 PMID: 12769713Ansari, 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. doi: 10.1002/ardp.202100179 PMID: 34467580Zhu, 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. doi: 10.3892/ol.2018.8873 PMID: 30008821Rai, 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. doi: 10.1007/s11030-021-10188-5 PMID: 33582935Parmar, 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. doi: 10.1016/j.bioorg.2021.105206 PMID: 34339975McKim, 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. doi: 10.2174/138620710790596736 PMID: 20053163Alanazi, 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. doi: 10.1080/14756366.2021.1945591 PMID: 34325596Idris, 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. doi: 10.1080/07391102.2020.1792346 PMID: 32672528Daoui, 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. doi: 10.1080/07391102.2022.2124456 PMID: 36120976Tabeshpour, 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. doi: 10.2174/1381612824666180903123423 PMID: 30179125Sodum, 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.Rim, K.T. In silico prediction of toxicity and its applications for chemicals at work. Toxicol. Environ. Health Sci., 2020, 12(3), 191-202. doi: 10.1007/s13530-020-00056-4 PMID: 32421081Tripathy, 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. doi: 10.1007/s00894-019-4238-6 PMID: 31773394Krishnan, 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. doi: 10.1021/acs.jcim.1c01319 PMID: 34792338Lin, 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. doi: 10.1016/j.phymed.2022.154481 PMID: 36215788Congreve, M.; Murray, C.W.; Blundell, T.L. Keynote review: Structural biology and drug discovery. Drug Discov. Today, 2005, 10(13), 895-907. doi: 10.1016/S1359-6446(05)03484-7 PMID: 15993809Hartenfeller, 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. doi: 10.1007/978-1-60761-839-3_12Papadopoulos, 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. doi: 10.1016/j.bmc.2021.116308 PMID: 34280849Shulga, 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. doi: 10.3390/molecules27061985 PMID: 35335347Ullah, 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. doi: 10.3389/fphar.2022.800970 PMID: 35250557Wu, 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. doi: 10.1038/s41586-023-05909-9 PMID: 37020023Smith, 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. doi: 10.1021/acs.jcim.5b00308 PMID: 26629886Kiss, G.; Röthlisberger, D.; Baker, D.; Houk, K.N. Evaluation and ranking of enzyme designs. Protein Sci., 2010, 19(9), 1760-1773. doi: 10.1002/pro.462 PMID: 20665693Krieger, 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. doi: 10.1002/prot.22570 PMID: 19768677Batool, M.; Ahmad, B.; Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci., 2019, 20(11), 2783. doi: 10.3390/ijms20112783 PMID: 31174387Emilien, 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. doi: 10.1093/qjmed/93.7.391 PMID: 10874050da 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. doi: 10.2174/1568026619666190816101948 PMID: 31418662Trott, 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.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. doi: 10.1093/pcmedi/pbab001 PMID: 33842834Chen, 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. doi: 10.1038/aps.2009.159 PMID: 19935678Giordano, D.; Biancaniello, C.; Argenio, M.A.; Facchiano, A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals, 2022, 15(5), 646. doi: 10.3390/ph15050646 PMID: 35631472Van Drie, J.H. Computer-aided drug design: the next 20 years. J. Comput. Aided Mol. Des., 2007, 21(10-11), 591-601. doi: 10.1007/s10822-007-9142-y PMID: 17989929León, 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. doi: 10.3390/ijms222413226 PMID: 34948022Michel, 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. doi: 10.3389/fchem.2020.00443 PMID: 32548091Jäntschi, L Prediction of physico-chemical and biological properties with the help of mathematical descriptors; Ian: Cluj-Napoca, 2000.Jäntschi, L. Molecular descriptors family on structure activity relationships 1. Review of the methodology. Leonardo J. Pract. Technol., 2005, 4(6), 76-98.Bolboacă, S.D.; Jäntschi, L. Comparison of QSAR performances on carboquinone derivatives. ScientificWorldJournal, 2009, 9(10), 1148-1166. doi: 10.1100/tsw.2009.131 PMID: 19838601Bolboacă, S.D.; Jäntschi, L. Nano-quantitative structure-property relationship modeling on C42 fullerene isomers. J. Chem., 2016, 2016, 1-8. doi: 10.1155/2016/1791756Wang, W.; He, S.; Dong, G.; Sheng, C. Nucleic-acid-based targeted degradation in drug discovery. J. Med. Chem., 2022, 65(15), 10217-10232. doi: 10.1021/acs.jmedchem.2c00875 PMID: 35916496Diller, 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. doi: 10.4155/fmc.15.142 PMID: 26510691Dong, D.; Xu, Z.; Zhong, W.; Peng, S. Parallelization of molecular docking: A review. Curr. Top. Med. Chem., 2018, 18(12), 1015-1028. doi: 10.2174/1568026618666180821145215 PMID: 30129415Wadood, 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. doi: 10.1080/13880209.2016.1225778 PMID: 27650666Liu, 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. doi: 10.1093/nsr/nww003 PMID: 32288960Pérez, 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. doi: 10.1007/s10822-013-9682-2 PMID: 24129820Jiang, 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. doi: 10.1002/cac2.12012 PMID: 32277744Huang, W.; Zhang, L.; Li, Z. Advances in computer-aided drug design for type 2 diabetes. Expert Opin. Drug Discov., 2022, 17(5), 461-472. doi: 10.1080/17460441.2022.2047644 PMID: 35254188Usha, 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. doi: 10.2174/1568026618666180101163651 PMID: 29295698Gurung, 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.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. doi: 10.1021/acs.accounts.9b00450 PMID: 31880912Zeb, 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. doi: 10.1021/acschemneuro.8b00405 PMID: 30407786Simon, 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. doi: 10.1007/s12539-016-0169-4 PMID: 27059855Shen, 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. doi: 10.2174/157340912803519615 PMID: 22734710Kale, 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. doi: 10.2174/1871527321666220401115529 PMID: 35366788Kesharwani, 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