The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

References

• Afantitis, A. (2020). Nanoinformatics: Artificial intelligence and nanotechnology in the new decade. Combinatorial Chemistry & High Throughput Screening, 23(1), 4–5. https://doi.org/10.2174/138620732301200316112000
   PubMed Web of Science ®Google Scholar
• Allach, S., Ben Ahmed, M., & Boudhir, A. A. (2021). Recognition and reconstruction of road marking with generative adversarial networks (gans). In Advances in Science, Technology and Innovation, 1, 219–225. https://doi.org/10.1007/978-3-030-53440-0_24
   Google Scholar
• Anderson, A. C. (2003). The process of structure-based drug design. Chemistry & Biology, 10(9), 787–797. (https://doi.org/10.1016/j.chembiol.2003.09.002
   PubMed Web of Science ®Google Scholar
• Arshia, A. H., Shadravan, S., Solhjoo, A., Sakhteman, A., & Sami, A. (2021). De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations. Computers in Biology and Medicine, 139, 104967. https://doi.org/10.1016/j.compbiomed.2021.104967
   PubMed Web of Science ®Google Scholar
• Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2021). MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Briefings in Bioinformatics, 22(3), 1–12. https://doi.org/10.1093/bib/bbaa161
   PubMed Web of Science ®Google Scholar
• Ben-Tal, N., Honig, B., Bagdassarian, C. K., & Ben-Shaul, A. (2000). Association entropy in adsorption processes. Biophysical Journal, 79(3), 1180–1187. https://doi.org/10.1016/S0006-3495(00)76372-7
   PubMed Web of Science ®Google Scholar
• Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the rule of 5 and drugability. Advanced Drug Delivery Reviews, 101, 89–98. https://doi.org/10.1016/j.addr.2016.05.007
   PubMed Web of Science ®Google Scholar
• Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
   PubMed Web of Science ®Google Scholar
• Biovia (2015). Dassault systemes BIOVIA, discovery studio modelling environment. Release 4.5. In Accelrys Software Inc. (4.5).
   Google Scholar
• Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing drug discovery via artificial intelligence. Trends in Pharmacological Sciences, 40(8), 592–604. https://doi.org/10.1016/j.tips.2019.06.004
   PubMed Web of Science ®Google Scholar
• Chen, F., Sun, H., Wang, J., Zhu, F., Liu, H., Wang, Z., Lei, T., Li, Y., & Hou, T. (2018). Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA (New York, N.Y.), 24(9), 1183–1194. https://doi.org/10.1261/rna.065896.118
   PubMed Web of Science ®Google Scholar
• Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J., Yu, T., Zhang, X., & Zhang, L. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet, 395(10223), 507–513. https://doi.org/10.1016/S0140-6736(20)30211-7
   PubMed Web of Science ®Google Scholar
• Cui, J., Li, F., & Shi, Z. L. (2019). Origin and evolution of pathogenic coronaviruses. Nature Reviews. Microbiology, 17(3), 181–192. https://doi.org/10.1038/s41579-018-0118-9
   PubMed Web of Science ®Google Scholar
• de Almeida, J. O., de Oliveira, V., R. T., Avelar, J. L. D. S., Moita, B. S., & Lima, L. M. (2020). COVID-19: Physiopathology and targets for therapeutic intervention COVID-19: Fisiopatologia e Alvos Para Intervenção Terapêutica. Revista Virtual de Quimica, 12(6), 1464–1497.
   Web of Science ®Google Scholar
• de Azevedo, W. F., Dias, R., Timmers, L. F. S. M., Pauli, I., Caceres, R. A., & Soares, M. B. P. (2009). Bioinformatics tools for screening of antiparasitic drugs. Current Drug Targets, 10(3), 232–239. https://doi.org/10.2174/138945009787581122
   PubMed Web of Science ®Google Scholar
• Devi, R. V., Sathya, S. S., & Coumar, M. S. (2015). Evolutionary algorithms for de novo drug design - A survey. Applied Soft Computing, 27, 543–552. https://doi.org/10.1016/j.asoc.2014.09.042
   Web of Science ®Google Scholar
• Diez, M., Petuya, V., Martínez-Cruz, L. A., & Hernández, A. (2014). Insights into mechanism kinematics for protein motion simulation. BMC Bioinformatics, 15(1), 184. https://doi.org/10.1186/1471-2105-15-184
   PubMedGoogle Scholar
• Dong, Y. W., Liao, M. L., Meng, X. L., & Somero, G. N. (2018). Structural flexibility and protein adaptation to temperature: Molecular dynamics analysis of malate dehydrogenases of marine molluscs. Proceedings of the National Academy of Sciences of the United States of America, 115(6), 1274–1279. https://doi.org/10.1073/pnas.1718910115
   PubMed Web of Science ®Google Scholar
• Dyabina, A. S., Radchenko, E. V., Palyulin, V. A., & Zefirov, N. S. (2016). Prediction of blood-brain barrier permeability of organic compounds. Doklady. Biochemistry and Biophysics, 470(1), 371–374. https://doi.org/10.1134/S1607672916050173
   PubMed Web of Science ®Google Scholar
• Emirik, M. (2022). Potential therapeutic effect of turmeric contents against SARS-CoV-2 compared with experimental COVID-19 therapies: In silico study. Journal of Biomolecular Structure & Dynamics, 40(5), 2024–2037. https://doi.org/10.1080/07391102.2020.1835719
   PubMed Web of Science ®Google Scholar
• Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of Cheminformatics, 1(1), 8. https://doi.org/10.1186/1758-2946-1-8
   PubMed Web of Science ®Google Scholar
• Farago, O. (2019). Langevin thermostat for robust configurational and kinetic sampling. Physica A: Statistical Mechanics and Its Applications, 534, 122210. https://doi.org/10.1016/j.physa.2019.122210
   Web of Science ®Google Scholar
• Fehr, A. R., & Perlman, S. (2015). Coronaviruses: An overview of their replication and pathogenesis. In Coronaviruses: Methods and Protocols. (1st ed., pp. 1–23) Springer. https://doi.org/10.1007/978-1-4939-2438-7_1
   Google Scholar
• Ferdian, P. R., Elfirta, R. R., Ikhwani, A. Z. N., Kasirah, K., Haerul, H., Sutardi, D., & Ruhiat, G. (2021). Studi in silico Senyawa Fenolik Madu sebagai Kandidat inhibitor Mpro SARS-CoV-2. Media Penelitian Dan Pengembangan Kesehatan, 31(3), 213–232. https://doi.org/10.22435/mpk.v31i3.4920
   Google Scholar
• Fricker, P. C., Gastreich, M., & Rarey, M. (2004). Automated drawing of structural molecular formulas under constraints. Journal of Chemical Information and Computer Sciences, 44(3), 1065–1078. https://doi.org/10.1021/ci049958u
   PubMedGoogle Scholar
• Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. ( https://doi.org/10.1517/17460441.2015.1032936
   PubMed Web of Science ®Google Scholar
• Guo, Y. R., Cao, Q. D., Hong, Z. S., Tan, Y. Y., Chen, S. D., Jin, H. J., Tan, K., Sen, Wang, D. Y., & Yan, Y. (2020). The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak- A n update on the status. Military Medical Research, 7(1), 11. https://doi.org/10.1186/s40779-020-00240-0
   PubMed Web of Science ®Google Scholar
• Gurung, A. B., Ali, M. A., Bhattacharjee, A., Abul Farah, M., Al-Hemaid, F., Abou-Tarboush, F. M., Al., Anazi, K. M., Al., Anazi, F. S., M., & Lee, J. (2016). Molecular docking of the anticancer bioactive compound proceraside with macromolecules involved in the cell cycle and DNA replication. Genetics and Molecular Research, 15(2), 1–8. https://doi.org/10.4238/gmr.15027829
   Web of Science ®Google Scholar
• Hevener, K. E., Zhao, W., Ball, D. M., Babaoglu, K., Qi, J., White, S. W., & Lee, R. E. (2009). Validation of molecular docking programs for virtual screening against dihydropteroate synthase. Journal of Chemical Information and Modeling, 49(2), 444–460. https://doi.org/10.1021/ci800293n
   PubMed Web of Science ®Google Scholar
• Hong, C., Cao, Q., Zhang, Z., Tsui, S. K.-W., & Yip, K. Y. (2022). Reusability report: Capturing properties of biological objects and their relationships using graph neural networks. Nature Machine Intelligence, 4(3), 222–226. https://doi.org/10.1038/s42256-022-00454-y
   Google Scholar
• Hua, Y., Zhang, D., & Ge, S. (2020). Research progress in the interpretability of deep learning models. Journal of Cyber Security, 5(3), 1-12. https://doi.org/10.19363/J.cnki.cn10-1380/tn.2020.05.01
   Google Scholar
• Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., De Groot, B. L., Grubmüller, H., & MacKerell, A. D. (2017). CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nature Methods, 14(1), 71–73. https://doi.org/10.1038/nmeth.4067
   PubMed Web of Science ®Google Scholar
• Hughes, J. D., Blagg, J., Price, D. A., Bailey, S., DeCrescenzo, G. A., Devraj, R. V., Ellsworth, E., Fobian, Y. M., Gibbs, M. E., Gilles, R. W., Greene, N., Huang, E., Krieger-Burke, T., Loesel, J., Wager, T., Whiteley, L., & Zhang, Y. (2008). Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorganic & Medicinal Chemistry Letters, 18(17), 4872–4875. https://doi.org/10.1016/j.bmcl.2008.07.071
   PubMed Web of Science ®Google Scholar
• Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5
   PubMedGoogle Scholar
• Ivanenkov, Y. A., Zagribelnyy, B. A., & Aladinskiy, V. A. (2019). Are we opening the door to a new era of medicinal chemistry or being collapsed to a chemical singularity? Journal of Medicinal Chemistry, 62(22), 10026–10043. https://doi.org/10.1021/acs.jmedchem.9b00004
   PubMed Web of Science ®Google Scholar
• Jain, S. K., & Agrawal, A. (2004). De novo drug design: An overview. In Indian Journal of Pharmaceutical Sciences, 66, Issue 6, 721–728.
   Google Scholar
• Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., … Yang, H. (2020). Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 582(7811), 289–293. https://doi.org/10.1038/s41586-020-2223-y
   PubMed Web of Science ®Google Scholar
• Jin, Z., Zhao, Y., Sun, Y., Zhang, B., Wang, H., Wu, Y., Zhu, Y., Zhu, C., Hu, T., Du, X., Duan, Y., Yu, J., Yang, X., Yang, X., Yang, K., Liu, X., Guddat, L. W., Xiao, G., Zhang, L., Yang, H., & Rao, Z. (2020). Structural basis for the inhibition of SARS-CoV-2 main protease by antineoplastic drug carmofur. Nature Structural & Molecular Biology, 27(6), 529–532. https://doi.org/10.1038/s41594-020-0440-6
   PubMed Web of Science ®Google Scholar
• Jo, S., Jiang, W., Lee, H. S., Roux, B., & Im, W. (2013). CHARMM-GUI ligand binder for absolute binding free energy calculations and its application. Journal of Chemical Information and Modeling, 53(1), 267–277. https://doi.org/10.1021/ci300505n
   PubMed Web of Science ®Google Scholar
• Johnson, T. W., Dress, K. R., & Edwards, M. (2009). Using the Golden Triangle to optimize clearance and oral absorption. Bioorganic & Medicinal Chemistry Letters, 19(19), 5560–5564. https://doi.org/10.1016/j.bmcl.2009.08.045
   PubMed Web of Science ®Google Scholar
• Kato, K., Nakayoshi, T., Kurimoto, E., & Oda, A. (2021). Molecular dynamics simulations for the protein–ligand complex structures obtained by computational docking studies using implicit or explicit solvents. Chemical Physics Letters, 781, 139022. https://doi.org/10.1016/j.cplett.2021.139022
   Web of Science ®Google Scholar
• Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M. K., Pei, J., Ting, M. Y. L., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., … Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122–1131.e9. https://doi.org/10.1016/j.cell.2018.02.010
   PubMed Web of Science ®Google Scholar
• Leach, A. R. (2006). Ligand-based approaches: Core molecular modeling. In Comprehensive Medicinal Chemistry II, 4, 87–118. ( https://doi.org/10.1016/b0-08-045044-x/00246-7
   Google Scholar
• Lee, J. W., Maria-Solano, M. A., Vu, T. N. L., Yoon, S., & Choi, S. (2022). Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochemical Society Transactions, 50(1), 241–252. ( https://doi.org/10.1042/BST20211240
   PubMed Web of Science ®Google Scholar
• Liao, Y. C., Tsai, M. H., Chen, F. C., & Hsiung, C. A. (2012). GEMSiRV: A software platform for GEnome-scale metabolic model simulation, reconstruction and visualization. Bioinformatics (Oxford, England), 28(13), 1752–1758. https://doi.org/10.1093/bioinformatics/bts267
   PubMed Web of Science ®Google Scholar
• Lima, A. H., Souza, P. R. M., Alencar, N., Lameira, J., Govender, T., Kruger, H. G., Maguire, G. E. M., & Alves, C. N. (2012). Molecular modeling of T. rangeli, T. brucei gambiense, and T. evansi sialidases in complex with the DANA inhibitor. Chemical Biology & Drug Design, 80(1), 114–120. https://doi.org/10.1111/j.1747-0285.2012.01380.x
   PubMed Web of Science ®Google Scholar
• Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today. Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
   PubMedGoogle Scholar
• Lipkowitz, K. B., Larter, R., & Cundari, T. R. (2005). Reviews in computational chemistry. In Reviews in computational chemistry. (Vol. 21). https://doi.org/10.1002/0471720895
   Google Scholar
• López-López, E., Naveja, J. J., & Medina-Franco, J. L. (2019). DataWarrior: An evaluation of the open-source drug discovery tool. Expert Opinion on Drug Discovery, 14(4), 335–341. https://doi.org/10.1080/17460441.2019.1581170
   PubMed Web of Science ®Google Scholar
• Lovering, F., Bikker, J., & Humblet, C. (2009). Escape from flatland: Increasing saturation as an approach to improving clinical success. Journal of Medicinal Chemistry, 52(21), 6752–6756. https://doi.org/10.1021/jm901241e
   PubMed Web of Science ®Google Scholar
• Martins-Filho, P., Ricardo, V., Gois-Santos, C., Tavares, S., Tavares, S., Melo, E. G., de, M., Nascimento-Júnior, E. M. d., & Santos, V. S. (2020). Recommendations for a safety dental care management during SARS-CoV-2 pandemic. Revista Panamericana de Salud Pública, 44, 1–3. https://doi.org/10.26633/RPSP.2020.51
   Web of Science ®Google Scholar
• Mazola, Y., Guirola, O., Palomares, S., Chinea, G., Menéndez, C., Hernández, L., & Musacchio, A. (2015). A comparative molecular dynamics study of thermophilic and mesophilic β-fructosidase enzymes. Journal of Molecular Modeling, 21(9), 1–15. https://doi.org/10.1007/s00894-015-2772-4
   PubMed Web of Science ®Google Scholar
• Miao, R., Xia, L. Y., Chen, H. H., Huang, H. H., & Liang, Y. (2019). Improved classification of blood-brain-barrier drugs using deep learning. Scientific Reports, 9(8802), 1–11. https://doi.org/10.1038/s41598-019-44773-4
   PubMedGoogle Scholar
• Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019). Deep learning for cellular image analysis. Nature Methods, 16(12), 1233–1246. https://doi.org/10.1038/s41592-019-0403-1
   PubMed Web of Science ®Google Scholar
• Morris, G. M., Ruth, H., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
   PubMed Web of Science ®Google Scholar
• Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021). Advances in de novo drug design: From conventional to machine learning methods. International Journal of Molecular Sciences, 22(4), 1676. https://doi.org/10.3390/ijms22041676
   PubMed Web of Science ®Google Scholar
• Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16), 1781–1802. https://doi.org/10.1002/jcc.20289
   PubMed Web of Science ®Google Scholar
• Pires, D. E. V., Kaminskas, L. M., & Ascher, D. B. (2018). Prediction and optimization of pharmacokinetic and toxicity properties of the ligand. Methods in Molecular Biology (Clifton, N.J.), 1762, 271–284. https://doi.org/10.1007/978-1-4939-7756-7_14
   PubMedGoogle Scholar
• Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112–134. https://doi.org/10.1016/j.jbi.2018.04.007
   PubMed Web of Science ®Google Scholar
• Rocha, M., Nunes da, M. M. M. A. M. R. T. E. S. M. H. S., & dos, S. (2022). Predictive ADMET study of rhodanine-3-acetic acid chalcone derivatives. Journal of the Indian Chemical Society, 99(7), 100535. https://doi.org/10.1016/j.jics.2022.100535
   Google Scholar
• Šakić, D., Šonjić, P., Tandarić, T., & Vrček, V. (2014). Chlorination of N-methylacetamide and amide-containing pharmaceuticals. Quantum-chemical study of the reaction mechanism. The Journal of Physical Chemistry. A, 118(12), 2367–2376. https://doi.org/10.1021/jp5012846
   PubMed Web of Science ®Google Scholar
• Schneider, G., & Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews. Drug Discovery, 4(8), 649–663. https://doi.org/10.1038/nrd1799
   PubMed Web of Science ®Google Scholar
• Scotti, L., Jaime Bezerra Mendonca Junior, F., Rodrigo Magalhaes Moreira, D., Sobral da Silva, M., R., Pitta, I., T., & Scotti, M. (2012). SAR, QSAR and docking of anticancer flavonoids and variants: A review. Current Topics in Medicinal Chemistry, 12(24), 2785–2809. https://doi.org/10.2174/1568026611212240007
   PubMed Web of Science ®Google Scholar
• Segler, M. H. S., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610. https://doi.org/10.1038/nature25978
   PubMed Web of Science ®Google Scholar
• Šponer, J., Hobza, P., & Leszczynski, J. (1999). Chapter 3 computational approaches to the studies of the interactions of nucleic acid bases. Theoretical and computational chemistry, 8, 85–117. (Issue C, https://doi.org/10.1016/S1380-7323(99)80078-8
   Google Scholar
• Stierand, K., Maass, P. C., & Rarey, M. (2006). Molecular complexes at a glance: Automated generation of two-dimensional complex diagrams. Bioinformatics (Oxford, England), 22(14), 1710–1716. https://doi.org/10.1093/bioinformatics/btl150
   PubMed Web of Science ®Google Scholar
• Tkatchenko, A. (2020). Machine learning for chemical discovery. Nature Communications, 11(1), 4125. https://doi.org/10.1038/s41467-020-17844-8
   PubMed Web of Science ®Google Scholar
• Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: Towards prediction paradise? Nature Reviews. Drug Discovery, 2(3), 192–204. https://doi.org/10.1038/nrd1032
   PubMed Web of Science ®Google Scholar
• Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D. (2010). CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry, 31(4), 671–690. https://doi.org/10.1002/jcc.21367
   PubMed Web of Science ®Google Scholar
• Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623. https://doi.org/10.1021/jm020017n
   PubMed Web of Science ®Google Scholar
• Verma, J., Khedkar, V., & Coutinho, E. (2010). 3D-QSAR in drug design - A review. Current Topics in Medicinal Chemistry, 10(1), 95–115. https://doi.org/10.2174/156802610790232260
   PubMed Web of Science ®Google Scholar
• Verma, S., Patel, C. N., & Chandra, M. (2021). Identification of novel inhibitors of SARS-CoV-2 main protease (Mpro) from Withania sp. by molecular docking and molecular dynamics simulation. Journal of Computational Chemistry, 42(26), 1861–1872. https://doi.org/10.1002/jcc.26717
   PubMed Web of Science ®Google Scholar
• Wager, T. T., Hou, X., Verhoest, P. R., & Villalobos, A. (2016). Central nervous system multiparameter optimization desirability: Application in drug discovery. ACS Chemical Neuroscience, 7(6), 767–775. https://doi.org/10.1021/acschemneuro.6b00029
   PubMed Web of Science ®Google Scholar
• Wang, C., Greene, D., Xiao, L., Qi, R., & Luo, R. (2017). Recent developments and applications of the MMPBSA method. Frontiers in Molecular Biosciences, 4(87), 87. (https://doi.org/10.3389/fmolb.2017.00087
   PubMedGoogle Scholar
• Wang, M., Wang, Z., Sun, H., Wang, J., Shen, C., Weng, G., Chai, X., Li, H., Cao, D., & Hou, T. (2022). Deep learning approaches for de novo drug design: An overview. In Current Opinion in Structural Biology, 72, 135–144. https://doi.org/10.1016/j.sbi.2021.10.001
   PubMed Web of Science ®Google Scholar
• World Health Organization. (2022). WHO health emergency dashboard WHO (COVID-19) homepage. Geneva: World Health Organization.
   Google Scholar
• Wright, D. W., Hall, B. A., Kenway, O. A., Jha, S., & Coveney, P. V. (2014). Computing clinically relevant binding free energies of HIV-1 protease inhibitors. Journal of Chemical Theory and Computation, 10(3), 1228–1241. https://doi.org/10.1021/ct4007037
   PubMed Web of Science ®Google Scholar
• Wu, D., Wu, T., Liu, Q., & Yang, Z. (2020). The SARS-CoV-2 outbreak: What we know. International Journal of Infectious Diseases: IJID: Official Publication of the International Society for Infectious Diseases, 94, 44–48. https://doi.org/10.1016/j.ijid.2020.03.004
   PubMed Web of Science ®Google Scholar
• Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., Becker, S., Rox, K., & Hilgenfeld, R. (2020). Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved a-ketoamide inhibitors. Science (New York, N.Y.), 368(6489), 409–412. https://doi.org/10.1126/science.abb3405
   PubMed Web of Science ®Google Scholar
• Zheng, M., Luo, X., Shen, Q., Wang, Y., Du, Y., Zhu, W., & Jiang, H. (2009). Site of metabolism prediction for six biotransformations mediated by cytochromes P450. Bioinformatics (Oxford, England), 25(10), 1251–1258. https://doi.org/10.1093/bioinformatics/btp140
   PubMed Web of Science ®Google Scholar
• Zumla, A., Hui, D. S., Azhar, E. I., Memish, Z. A., & Maeurer, M. (2020). Reducing mortality from 2019-nCoV: Host-directed therapies should be an option. The Lancet, 395(10224), e35–e36. https://doi.org/10.1016/S0140-6736(20)30305-6
   PubMed Web of Science ®Google Scholar