Останні досягнення у комп’ютерному дизайні ліків для терапії проти коронавірус SARS-CoV-2
DOI:
https://doi.org/10.15587/2519-4852.2023.290318Ключові слова:
коронавірус людини, QSAR, молекулярний докінг, віртуальний скринінг, машинне навчання, молекулярна динаміка, структурний дизайн ліків, протеази Mpro та PLpro, CADD, SARS-CоV-2Анотація
Незважаючи на значні експериментальні зусилля, зосереджені на вивченні важкого гострого респіраторного синдрому, викликаного коронавірусом 2 (SARS-CoV-2), методи обчислювальної хімії є перспективними додатковими інструментами в боротьбі з коронавірусом 2019 (COVID-19). Цей огляд має на меті ознайомити читачів з останнім прогресом і досягненнями в обчислювальних підходах, які зараз використовуються для оптимізації відкриття та розробки ліків у контексті COVID-19. Наш огляд має подвійне призначення. Він призначений (а) ознайомити читачів із загальною концепцією QSAR, скринінгу in silico, молекулярного докінгу та моделювання молекулярної динаміки (МД) і (б) надати ключові приклади нещодавнього застосування цих обчислювальних інструментів у відкритті нових терапевтичних засобів та агентів проти COVID-19. В роботі показано, як конструювання ліків на основі структури та ліганду може прискорити структурне з’ясування фармакологічного націлювання на ліки та відкриття доклінічних молекул препаратів-кандидатів. Кілька прикладів обчислювальних досліджень МД демонструють, як атомістичне моделювання МД може полегшити наше розуміння молекулярної основи дії ліків і біологічних механізмів інгібування вірусу в атомних деталях. Нарешті, обговорюються короткострокові та довгострокові перспективи комп’ютерного відкриття ліків.
Мета цього дослідження полягає в тому, щоб підсумувати прогрес і досягнення за останні три роки в обчислювальних підходах, які зараз використовуються для оптимізації процесу відкриття та розробки ліків у контексті COVID-19.
Матеріали та методи. Огляд літератури щодо QSAR, скринінгу in silico, машинного навчання, молекулярного докінгу та моделювання молекулярної динаміки (MD) у контексті COVID-19. Літературний пошук проводився за допомогою онлайн-баз даних, таких як Scopus, Web of Science, PDB-protein databank і PubMed, зосереджуючись на таких ключових словах: людський коронавірус, QSAR, молекулярний докінг, віртуальний скринінг, машинне навчання, молекулярна динаміка, протеази Mpro та PLpro, SARS-CоV-2.
Результати. Огляд ознайомлює читачів із загальною концепцією QSAR, скринінгу in silico, машинного навчання, молекулярного докінгу та моделювання МД, а також надає ключові приклади нещодавнього застосування цих обчислювальних інструментів у відкритті нових терапевтичних засобів проти COVID-19.
Висновки. Показані нові досягнення у використанні комп’ютеризованих методів дизайну та відкриття ліків для терапевтичних засобів проти SARS-CoV-2
Спонсор дослідження
- Grant No. 42/0062 (2021.01/0062) “Molecular design, synthesis and screening of new potential antiviral pharmaceutical ingredients for the treatment of infectious diseases COVID-19” from the National Research Foundation of Ukraine.
Посилання
- Vincenzi, M., Mercurio, F. A., Leone, M. (2023). Looking for SARS-CoV-2 Therapeutics Through Computational Approaches. Current Medicinal Chemistry, 30 (28), 3158–3214. doi: https://doi.org/10.2174/0929867329666221004104430
- Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D. et al. (2021). A critical overview of computational approaches employed for COVID-19 drug discovery. Chemical Society Reviews, 50 (16), 9121–9151. doi: https://doi.org/10.1039/d0cs01065k
- Middha, S. K., Usha, T., Sukhralia, S., Pareek, C., Yadav, R., Agnihotri, R. et al.; Parihar, A., Khan, R., Kumar, A., Kaushik, A. K., Gohel, H. (Eds.) (2022). Prediction of drug-target interaction – a helping hand in drug repurposing. Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection. Academic Press, 519–536. doi: https://doi.org/10.1016/b978-0-323-91172-6.00006-6
- Nandi, S., Nayak, B. S., Khede, M. K., Saxena, A. K. (2022). Repurposing of Chemotherapeutics to Combat COVID-19. Current Topics in Medicinal Chemistry, 22 (32), 2660–2694. doi: https://doi.org/10.2174/1568026623666221130142517
- Yadav, M., Dhagat, S., Eswari, J. S. (2020). Emerging strategies on in silico drug development against COVID-19: challenges and opportunities. European Journal of Pharmaceutical Sciences, 155, 105522. doi: https://doi.org/10.1016/j.ejps.2020.105522
- Gao, K., Wang, R., Chen, J., Cheng, L., Frishcosy, J., Huzumi, Y. et al. (2022). Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chemical Reviews, 122 (13), 11287–11368. doi: https://doi.org/10.1021/acs.chemrev.1c00965
- Kumar, S., Kovalenko, S., Bhardwaj, S., Sethi, A., Gorobets, N. Yu., Desenko, S. M. et al. (2022). Drug repurposing against SARS-CoV-2 using computational approaches. Drug Discovery Today, 27 (7), 2015–2027. doi: https://doi.org/10.1016/j.drudis.2022.02.004
- Sadybekov, A. V., Katritch, V. (2023). Computational approaches streamlining drug discovery. Nature, 616 (7958), 673–685. doi: https://doi.org/10.1038/s41586-023-05905-z
- Shah, A., Jain, M.; Rudrapal, M., Egbuna, C. (Eds.) (2022). Limitations and future challenges of computer-aided drug design methods. Computer Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches. Elsevier, 283–297. doi: https://doi.org/10.1016/b978-0-323-90608-1.00006-x
- Chen, L., Fan, Z., Chang, J., Yang, R., Hou, H., Guo, H. et al. (2023). Sequence-based drug design as a concept in computational drug design. Nature Communications, 14 (1). doi: https://doi.org/10.1038/s41467-023-39856-w
- Bon, M., Bilsland, A., Bower, J., McAulay, K. (2022). Fragment‐based drug discovery –the importance of high‐quality molecule libraries. Molecular Oncology, 16 (21), 3761–3777. doi: https://doi.org/10.1002/1878-0261.13277
- Green, H., Koes, D. R., Durrant, J. D. (2021). DeepFrag: a deep convolutional neural network for fragment-based lead optimization. Chemical Science, 12 (23), 8036–8047. doi: https://doi.org/10.1039/d1sc00163a
- Shi, X.-X., Wang, Z.-Z., Wang, F., Hao, G.-F., Yang, G.-F. (2023). ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy. Nucleic Acids Research, 51 (W1), W25–W32. doi: https://doi.org/10.1093/nar/gkad348
- Gurung, A. B., Ali, M. A., Lee, J., Farah, M. A., Al-Anazi, K. M. (2021). An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19. BioMed Research International, 2021, 1–18. doi: https://doi.org/10.1155/2021/8853056
- Bajad, N. G., Rayala, S., Gutti, G., Sharma, A., Singh, M., Kumar, A., Singh, S. K. (2021). Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2. Current Research in Pharmacology and Drug Discovery, 2, 100026. doi: https://doi.org/10.1016/j.crphar.2021.100026
- Zhang, S., Krumberger, M., Morris, M. A., Parrocha, C. M. T., Kreutzer, A. G., Nowick, J. S. (2021). Structure-based drug design of an inhibitor of the SARS-CoV-2 (COVID-19) main protease using free software: A tutorial for students and scientists. European Journal of Medicinal Chemistry, 218, 113390. doi: https://doi.org/10.1016/j.ejmech.2021.113390
- Varela‐Rial, A., Majewski, M., De Fabritiis, G. (2021). Structure based virtual screening: Fast and slow. WIREs Computational Molecular Science, 12 (2). doi: https://doi.org/10.1002/wcms.1544
- Chen, W., Liu, X., Zhang, S., Chen, S. (2023). Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular Therapy – Nucleic Acids, 31, 691–702. doi: https://doi.org/10.1016/j.omtn.2023.02.019
- Nasser, M., Yusof, U. K., Salim, N. (2023). Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review. Processes, 11 (5). doi: https://doi.org/10.3390/pr11051340
- Amaro, R. E., Mulholland, A. J. (2018). Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nature Reviews Chemistry, 2 (4). doi: https://doi.org/10.1038/s41570-018-0148
- Amin, Sk. A., Banerjee, S., Singh, S., Qureshi, I. A., Gayen, S., Jha, T. (2021). First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery. Molecular Diversity, 25 (3), 1827–1838. doi: https://doi.org/10.1007/s11030-020-10166-3
- Luo, D., Tong, J.-B., Zhang, X., Xiao, X.-C., Bian, S. (2022). Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19. Journal of Molecular Structure, 1247, 131378. doi: https://doi.org/10.1016/j.molstruc.2021.131378
- Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17 (2/4), 241–253. doi: https://doi.org/10.1023/a:1025386326946
- Golbraikh, A., Tropsha, A. (2000). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Molecular Diversity, 5 (4), 231–243. doi: https://doi.org/10.1023/a:102137210868
- Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P. et al. (2005). Virtual Computational Chemistry Laboratory – Design and Description. Journal of Computer-Aided Molecular Design, 19 (6), 453–463. doi: https://doi.org/10.1007/s10822-005-8694-y
- Yap, C. W. (2011). PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32 (7), 1466–1474. doi: https://doi.org/10.1002/jcc.21707
- Todeschini, R., Consonni, V. (2000). Handbook of Molecular Descriptors. Weinheim: Wiley-VCH. doi: https://doi.org/10.1002/9783527613106
- Devillers, J., Balaban, A. T. (1999). Topological Indices and Related Descriptors in QSAR and QSPR. London: CRC Press, 811. doi: https://doi.org/10.1201/9781482296945
- Malinowski, E. R. (2002). Factor Analysis in Chemistry. Wiley-VCH, 432.
- Devillers, J. (1996). Genetic Algorithms in Molecular Modeling: Academic Press. doi: https://doi.org/10.1016/b978-0-12-213810-2.x5000-2
- Solnon, C., Jussien, N. (2013). Ant Colony Optimization and Constraint Programming. John Wiley & Sons. doi: https://doi.org/10.1002/9781118557563
- Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi: https://doi.org/10.1023/a:1010933404324
- Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58 (1), 267–288. doi: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
- Hesterberg, T., Choi, N. H., Meier, L., Fraley, C. (2008). Least angle and ℓ1 penalized regression: A review. Statistics Surveys, 2, 61–93. doi: https://doi.org/10.1214/08-ss035
- Zhao, P., Yu, B. (2007). Stagewise Lasso. Journal of Machine Learning Research, 8, 2701–2726.
- Hastie, T., Tibshirani, R. (2004). Efficient quadratic regularization for expression arrays. Biostatistics, 5 (3), 329–340. doi: https://doi.org/10.1093/biostatistics/kxh010
- Roy, K., Kar, S., Das, R. N. (2015). A Primer on QSAR/QSPR Modeling: Fundamental Concepts. Cham: Springer International Publishing. doi: https://doi.org/10.1007/978-3-319-17281-1
- Khaldan, A., Bouamrane, S., En-Nahli, F., El-mernissi, R., El khatabi, K., Hmamouchi, R. et al. (2021). Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties. Heliyon, 7 (3), e06603. doi: https://doi.org/10.1016/j.heliyon.2021.e06603
- Jayakumar, J., Ebanesar, A., Gautam, S. (2023). Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach. Toxicology and Environmental Health Sciences, 15 (2), 109–117. doi: https://doi.org/10.1007/s13530-023-00172-x
- Beger, R. D., Young, J. F., Fang, H. (2004). Discriminant Function Analyses of Liver-Specific Carcinogens. Journal of Chemical Information and Computer Sciences, 44 (3), 1107–1110. doi: https://doi.org/10.1021/ci0342829
- Hosmer, D. W. J., Lemeshow, S. (2000). Applied Logistic Regression. John Wiley & Sons. doi: https://doi.org/10.1002/0471722146
- Xu, Y., Zomer, S., Brereton, R. G. (2006). Support Vector Machines: A Recent Method for Classification in Chemometrics. Critical Reviews in Analytical Chemistry, 36 (3-4), 177–188. doi: https://doi.org/10.1080/10408340600969486
- Herbrich, R. (2001). Learning Kernel Classifiers Theory and Algorithms. Cambridge: The MIT Press. doi: https://doi.org/10.7551/mitpress/4170.001.0001
- Müller, K.-R., Rätsch, G., Sonnenburg, S., Mika, S., Grimm, M., Heinrich, N. (2005). Classifying ‘Drug-likeness' with Kernel-Based Learning Methods. Journal of Chemical Information and Modeling, 45 (2), 249–253. doi: https://doi.org/10.1021/ci049737o
- Rokach, L., Maimon, O. (2014). Data Mining with Decision Trees. Vol. 81. World Scientific, 328. doi: https://doi.org/10.1142/9097
- Santana, M. V. S., Silva-Jr, F. P.; Parihar, A., Khan, R., Kumar, A., Kaushik, A. K., Gohel, H. (Eds.) (2022). Artificial intelligence methods to repurpose and discover new drugs to fight the Coronavirus disease-2019 pandemic. Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection. Academic Press, 537–557. doi: https://doi.org/10.1016/b978-0-323-91172-6.00016-9
- Krishnamurthy, A., Goel, P.; Parihar, A., Khan, R., Kumar, A., Kaushik, A. K., Gohel, H. (Eds.) (2022). Artificial intelligence-based drug screening and drug repositioning tools and their application in the present scenario. Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection. Academic Press, 379–398. doi: https://doi.org/10.1016/b978-0-323-91172-6.00010-8
- Murcia-Soler, M., Pérez-Giménez, F., García-March, F. J., Salabert-Salvador, M. T., Díaz-Villanueva, W., Castro-Bleda, M. J., Villanueva-Pareja, A. (2004). Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds. Journal of Chemical Information and Computer Sciences, 44 (3), 1031–1041. doi: https://doi.org/10.1021/ci030340e
- Gabrielsson, R. B., Carlsson, G. (2019). Exposition and Interpretation of the Topology of Neural Networks. Paper presented at the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). doi: https://doi.org/10.1109/icmla.2019.00180
- Skinnider, M. A., Stacey, R. G., Wishart, D. S., Foster, L. J. (2021). Chemical language models enable navigation in sparsely populated chemical space. Nature Machine Intelligence, 3 (9), 759–770. doi: https://doi.org/10.1038/s42256-021-00368-1
- Tay, D. W. P., Yeo, N. Z. X., Adaikkappan, K., Lim, Y. H., Ang, S. J. (2023). 67 million natural product-like compound database generated via molecular language processing. Scientific Data, 10 (1). doi: https://doi.org/10.1038/s41597-023-02207-x
- Chen, H., Bajorath, J. (2023). Designing highly potent compounds using a chemical language model. Scientific Reports, 13 (1). doi: https://doi.org/10.1038/s41598-023-34683-x
- Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R., Jensen, K. F. (2022). Generative models for molecular discovery: Recent advances and challenges. WIREs Computational Molecular Science, 12 (5). doi: https://doi.org/10.1002/wcms.1608
- Pyzer-Knapp, E., Laino, T., Grisafi, A., Wilkins, D., Willatt, M., Ceriotti, M. et al. (2019). Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions. American Chemical Society. doi: https://doi.org/10.1021/bk-2019-1326
- Varmuza, K., Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton: CRC Press, 336. doi: https://doi.org/10.1201/9781420059496
- Pen, L. Z., Xian Xian, K., Yew, C. F., Hau, O. S., Sumari, P., Abualigah, L. et al. (2023). Classification Applications with Deep Learning and Machine Learning Technologies. Cham: Springer International Publishing. doi: https://doi.org/10.1007/978-3-031-17576-3
- Veerasamy, R., Rajak, H., Jain, A. K., Sivadasan, S., Varghese, C., K., A. R. (2011). Validation of QSAR Models-Strategies and Importance. International Journal of Drug Design and Discovery, 2 (3), 511–518.
- Alexander, D. L. J., Tropsha, A., Winkler, D. A. (2015). Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. Journal of Chemical Information and Modeling, 55 (7), 1316–1322. doi: https://doi.org/10.1021/acs.jcim.5b00206
- Consonni, V., Ballabio, D., Todeschini, R. (2009). Comments on the Definition of the Q2 Parameter for QSAR Validation. Journal of Chemical Information and Modeling, 49 (7), 1669–1678. doi: https://doi.org/10.1021/ci900115y
- Liu, H., Cocea, M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2 (4), 357–386. doi: https://doi.org/10.1007/s41066-017-0049-2
- Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15 (4), 531–538. doi: https://doi.org/10.1002/sam.11583
- Arlot, S., Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79. doi: https://doi.org/10.1214/09-ss054
- Espinosa, G., Arenas, A., Giralt, F. (2001). Fundamentals of Molecular Similarity. Boston: Springer US.
- Mestres, J., Rohrer, D. C., Maggiora, G. M. (1997). MIMIC: A molecular-field matching program. Exploiting applicability of molecular similarity approaches. Journal of Computational Chemistry, 18 (7), 934–954. doi: https://doi.org/10.1002/(sici)1096-987x(199705)18:7<934::aid-jcc6>3.0.co;2-s
- Klebe, G. (Ed.) (2007). Virtual Screening: An Alternative or Complement to High Throughput Screening? Kluwer Academic Publishers.
- Bajusz, D., Rácz, A., Héberger, K.; Chackalamannil, S., Rotella, D., Ward, S. E. (Eds.) (2017). Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching. Comprehensive Medicinal Chemistry III. Oxford: Elsevier, 329–378. doi: https://doi.org/10.1016/b978-0-12-409547-2.12345-5
- Capecchi, A., Probst, D., Reymond, J.-L. (2020). One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of Cheminformatics, 12 (1). doi: https://doi.org/10.1186/s13321-020-00445-4
- Willett, P. (2006). Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today, 11 (23-24), 1046–1053. doi: https://doi.org/10.1016/j.drudis.2006.10.005
- Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T. et al. (2012). Recognizing Pitfalls in Virtual Screening: A Critical Review. Journal of Chemical Information and Modeling, 52 (4), 867–881. doi: https://doi.org/10.1021/ci200528d
- Landrum, G. (2020). RDKit: Open-source cheminformatics. Available at: https://www.rdkit.org/
- Alves, V. M., Bobrowski, T., Melo‐Filho, C. C., Korn, D., Auerbach, S., Schmitt, C. et al. (2020). QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2. Molecular Informatics, 40 (1). doi: https://doi.org/10.1002/minf.202000113
- Morgan, H. L. (1965). The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. Journal of Chemical Documentation, 5 (2), 107–113. doi: https://doi.org/10.1021/c160017a018
- Willett, P. (2003). Similarity-based approaches to virtual screening. Biochemical Society Transactions, 31 (3), 603–606. doi: https://doi.org/10.1042/bst0310603
- Raymond, J. W., Willett, P. (2002). Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. Journal of Computer-Aided Molecular Design, 16 (1), 59–71. doi: https://doi.org/10.1023/a:1016387816342
- Gossen, J., Albani, S., Hanke, A., Joseph, B. P., Bergh, C., Kuzikov, M. et al. (2021). A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics. ACS Pharmacology & Translational Science, 4 (3), 1079–1095. doi: https://doi.org/10.1021/acsptsci.0c00215
- Nikolova, N., Jaworska, J. (2003). Approaches to Measure Chemical Similarity – a Review. QSAR & Combinatorial Science, 22 (9-10), 1006–1026. Portico. doi: https://doi.org/10.1002/qsar.200330831
- Stumpfe, D., Ripphausen, P., Bajorath, J. (2012). Virtual compound screening in drug discovery. Future Medicinal Chemistry, 4 (5), 593–602. doi: https://doi.org/10.4155/fmc.12.19
- Giordano, D., Biancaniello, C., Argenio, M. A., Facchiano, A. (2022). Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals, 15 (5), 646. doi: https://doi.org/10.3390/ph15050646
- Glaab, E., Manoharan, G. B., Abankwa, D. (2021). Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors. Journal of Chemical Information and Modeling, 61 (8), 4082–4096. doi: https://doi.org/10.1021/acs.jcim.1c00258
- Wermuth, C. G., Ganellin, C. R., Lindberg, P., Mitscher, L. A. (1998). Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure and Applied Chemistry, 70 (5), 1129–1143. doi: https://doi.org/10.1351/pac199870051129
- Molecular Operating Environment (MOE). (2023). 2022.02 Chemical Computing Group ULC. Montreal.
- Wolber, G., Langer, T. (2004). LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters. Journal of Chemical Information and Modeling, 45 (1), 160–169. doi: https://doi.org/10.1021/ci049885e
- Brooijmans, N., Kuntz, I. D. (2003). Molecular Recognition and Docking Algorithms. Annual Review of Biophysics and Biomolecular Structure, 32 (1), 335–373. doi: https://doi.org/10.1146/annurev.biophys.32.110601.142532
- Weiner, S. J., Kollman, P. A., Nguyen, D. T., Case, D. A. (1986). An all atom force field for simulations of proteins and nucleic acids. Journal of Computational Chemistry, 7 (2), 230–252. doi: https://doi.org/10.1002/jcc.540070216
- Gohlke, H., Hendlich, M., Klebe, G. (2000). Knowledge-based scoring function to predict protein-ligand interactions. Journal of Molecular Biology, 295 (2), 337–356. doi: https://doi.org/10.1006/jmbi.1999.3371
- Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., Mee, R. P. (1997). Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer-Aided Molecular Design, 11 (5), 425–445. doi: https://doi.org/10.1023/a:1007996124545
- Alex, A., Finn, P. (1997). Fast and accurate predictions of relative binding energies. Journal of Molecular Structure: THEOCHEM, 398-399, 551–554. doi: https://doi.org/10.1016/s0166-1280(97)00031-6
- Ballester, P. J., Mitchell, J. B. O. (2010). A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics, 26 (9), 1169–1175. doi: https://doi.org/10.1093/bioinformatics/btq112
- Guedes, I. A., Barreto, A. M. S., Marinho, D., Krempser, E., Kuenemann, M. A., Sperandio, O. et al. (2021). New machine learning and physics-based scoring functions for drug discovery. Scientific Reports, 11 (1). doi: https://doi.org/10.1038/s41598-021-82410-1
- Charifson, P. S., Corkery, J. J., Murcko, M. A., Walters, W. P. (1999). Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. Journal of Medicinal Chemistry, 42 (25), 5100–5109. doi: https://doi.org/10.1021/jm990352k
- Kumar, T. D. A. (2022). Drug Design. A Conceptual Overview. Abingdon: CRC Press. doi: https://doi.org/10.1201/9781003298755
- Kitchen, D. B., Decornez, H., Furr, J. R., Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery, 3 (11), 935–949. doi: https://doi.org/10.1038/nrd1549
- Warren, G. L., Andrews, C. W., Capelli, A.-M., Clarke, B., LaLonde, J., Lambert, M. H. et al. (2005). A Critical Assessment of Docking Programs and Scoring Functions. Journal of Medicinal Chemistry, 49 (20), 5912–5931. doi: https://doi.org/10.1021/jm050362n
- Stahl, M., Rarey, M. (2001). Detailed Analysis of Scoring Functions for Virtual Screening. Journal of Medicinal Chemistry, 44 (7), 1035–1042. doi: https://doi.org/10.1021/jm0003992
- Zhu, H., Zhou, R., Cao, D., Tang, J., Li, M. (2023). A pharmacophore-guided deep learning approach for bioactive molecular generation. Nature Communications, 14 (1). doi: https://doi.org/10.1038/s41467-023-41454-9
- Mazanetz, M., Law, R., Whittaker, M. (2013). Hit and Lead Identification from Fragments. De novo Molecular Design. Weinheim: Wiley-VCH Verlag GmbH & Co.KGaA, 143–200. doi: https://doi.org/10.1002/9783527677016.ch6
- Wetzel, S., Klein, K., Renner, S., Rauh, D., Oprea, T. I., Mutzel, P., Waldmann, H. (2009). Interactive exploration of chemical space with Scaffold Hunter. Nature Chemical Biology, 5 (8), 581–583. doi: https://doi.org/10.1038/nchembio.187
- Langevin, M., Minoux, H., Levesque, M., Bianciotto, M. (2020). Scaffold-Constrained Molecular Generation. Journal of Chemical Information and Modeling, 60 (12), 5637–5646. doi: https://doi.org/10.1021/acs.jcim.0c01015
- Scott, O. B., Edith Chan, A. W. (2020). ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees. Bioinformatics, 36 (12), 3930–3931. doi: https://doi.org/10.1093/bioinformatics/btaa219
- Schaub, J., Zander, J., Zielesny, A., Steinbeck, C. (2022). Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK). Journal of Cheminformatics, 14 (1). doi: https://doi.org/10.1186/s13321-022-00656-x
- Buckle, D. R., Erhardt, P. W., Ganellin, C. R., Kobayashi, T., Perun, T. J., Proudfoot, J., Senn-Bilfinger, J.; Desai, M. C. (Ed.) (2013). Glossary of Terms Used in Medicinal Chemistry Part II (IUPAC Recommendations 2013). Annual Reports in Medicinal Chemistry. Academic Press, 48, 387–418. doi: https://doi.org/10.1016/b978-0-12-417150-3.00024-7
- Hessler, G., Baringhaus, K.-H. (2010). The scaffold hopping potential of pharmacophores. Drug Discovery Today: Technologies, 7 (4), e263–e269. doi: https://doi.org/10.1016/j.ddtec.2010.09.001
- Brown, N. (Ed.) (2013). Scaffold Hopping in Medicinal Chemistry. Wiley-VCH. doi: https://doi.org/10.1002/9783527665143
- Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., Olson, A. J. (2016). Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11 (5), 905–919. doi: https://doi.org/10.1038/nprot.2016.051
- Goodsell, D. S., Morris, G. M., Olson, A. J. (1996). Automated docking of flexible ligands: Applications of AutoDock. Journal of Molecular Recognition, 9 (1), 1–5. doi: https://doi.org/10.1002/(sici)1099-1352(199601)9:1<1::aid-jmr241>3.0.co;2-6
- Trott, O., Olson, A. J. (2009). 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. doi: https://doi.org/10.1002/jcc.21334
- Eberhardt, J., Santos-Martins, D., Tillack, A. F., Forli, S. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling, 61 (8), 3891–3898. doi: https://doi.org/10.1021/acs.jcim.1c00203
- Jones, G., Willett, P., Glen, R. C., Leach, A. R., Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267 (3), 727–748. doi: https://doi.org/10.1006/jmbi.1996.0897
- Grosdidier, A., Zoete, V., Michielin, O. (2011). SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Research, 39, W270–W277. doi: https://doi.org/10.1093/nar/gkr366
- Kramer, B., Rarey, M., Lengauer, T. (1999). Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins: Structure, Function, and Genetics, 37 (2), 228–241. doi: https://doi.org/10.1002/(sici)1097-0134(19991101)37:2<228::aid-prot8>3.0.co;2-8
- Saeedi, S., Rahmati, A., Chavoshpour-Natanzi, Z. (2022). Synthesis of pyrazolo[5′,1′:2,3]imidazo[1,5-c]quinazolin-6(5H)-ones and molecular docking study of their affinity against the COVID-19 main protease. RSC Advances, 12 (30), 19579–19589. doi: https://doi.org/10.1039/d2ra03179e
- Amendola, G., Ettari, R., Previti, S., Di Chio, C., Messere, A., Di Maro, S. et al. (2021). Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening. Journal of Chemical Information and Modeling, 61 (4), 2062–2073. doi: https://doi.org/10.1021/acs.jcim.1c00184
- Porto, V. A., Porto, R. S. (2022). In silico Studies of Novel Synthetic Compounds as Potential Drugs to Inhibit Coronavirus (SARS-CoV-2): A Systematic Review. Biointerface Research in Applied Chemistry, 12 (4), 4293–4306. doi: https://doi.org/10.33263/briac124.42934306
- Hou, N., Shuai, L., Zhang, L., Xie, X., Tang, K., Zhu, Y. et al. (2023). Development of Highly Potent Noncovalent Inhibitors of SARS-CoV-2 3CLpro. ACS Central Science, 9 (2), 217–227. doi: https://doi.org/10.1021/acscentsci.2c01359
- Evseeva, L. V., Ivanov, V. V., Karpina, V. R., Kovalenko, S. S., Kuznetsov, I. E., Langer, T. et al. (2020). The virtual screening application for searching potential antiviral agents to treat COVID-19 disease. Journal of Organic and Pharmaceutical Chemistry, 18 (2 (70)), 3–15. doi: https://doi.org/10.24959/ophcj.20.200019
- Kulkarni, S. A., Nagarajan, S. K., Ramesh, V., Palaniyandi, V., Selvam, S. P., Madhavan, T. (2020). Computational evaluation of major components from plant essential oils as potent inhibitors of SARS-CoV-2 spike protein. Journal of Molecular Structure, 1221, 128823. doi: https://doi.org/10.1016/j.molstruc.2020.128823
- Ahmad, M., Dwivedy, A., Mariadasse, R., Tiwari, S., Kar, D., Jeyakanthan, J., Biswal, B. K. (2020). Prediction of Small Molecule Inhibitors Targeting the Severe Acute Respiratory Syndrome Coronavirus-2 RNA-dependent RNA Polymerase. ACS Omega, 5 (29), 18356–18366. doi: https://doi.org/10.1021/acsomega.0c02096
- Lohachova, K. O., Sviatenko, A. S., Kyrychenko, A., Ivanov, V. V., Langer, T., Kovalenko, S. M., Kalugin, O. N. (2023). Computer-aided drug design of novel nirmatrelvir analogs inhibiting main protease of Coronavirus SARS-CoV-2. Journal of Applied Pharmaceutical Science, 13. In press
- Tejera, E., Munteanu, C. R., López-Cortés, A., Cabrera-Andrade, A., Pérez-Castillo, Y. (2020). Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease. Molecules, 25 (21), 5172. doi: https://doi.org/10.3390/molecules25215172
- Pathak, N., Chen, Y.-T., Hsu, Y.-C., Hsu, N.-Y., Kuo, C.-J., Tsai, H. P. (2020). Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing. ACS Nano, 15 (1), 857–872. doi: https://doi.org/10.1021/acsnano.0c07383
- Zhang, C.-H., Stone, E. A., Deshmukh, M., Ippolito, J. A., Ghahremanpour, M. M., Tirado-Rives, J. et al. (2021). Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations. ACS Central Science, 7 (3), 467–475. doi: https://doi.org/10.1021/acscentsci.1c00039
- Salo-Ahen, O. M. H., Alanko, I., Bhadane, R., Bonvin, A. M. J. J., Honorato, R. V., Hossain, S. et al. (2020). Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes, 9 (1), 71. doi: https://doi.org/10.3390/pr9010071
- Allen, M. P., Tildesley, D. J. (2017). Computer Simulation of Liquids: Second Edition. Oxford: Oxford University Press. doi: https://doi.org/10.1093/oso/9780198803195.001.0001
- Christen, M., Hünenberger, P. H., Bakowies, D., Baron, R., Bürgi, R., Geerke, D. P. et al. (2005). The GROMOS software for biomolecular simulation: GROMOS05. Journal of Computational Chemistry, 26 (16), 1719–1751. doi: https://doi.org/10.1002/jcc.20303
- Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., Berendsen, H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26 (16), 1701–1718. doi: https://doi.org/10.1002/jcc.20291
- Kalé, L., Skeel, R., Bhandarkar, M., Brunner, R., Gursoy, A., Krawetz, N. et al. (1999). NAMD2: Greater Scalability for Parallel Molecular Dynamics. Journal of Computational Physics, 151 (1), 283–312. doi: https://doi.org/10.1006/jcph.1999.6201
- Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S., Karplus, M. (1983). CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry, 4 (2), 187–217. doi: https://doi.org/10.1002/jcc.540040211
- Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M. et al. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26 (16), 1668–1688. doi: https://doi.org/10.1002/jcc.20290
- Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S. et al. (2022). LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications, 271, 108171. doi: https://doi.org/10.1016/j.cpc.2021.108171
- Smith, W., Yong, C. W., Rodger, P. M. (2002). DL_POLY: Application to molecular simulation. Molecular Simulation, 28 (5), 385–471. doi: https://doi.org/10.1080/08927020290018769
- Van Der Spoel, D., Lindahl, E., Hess, B., van Buuren, A. R., Apol, E., Meulenhoff, P. J. et al. (2010). Gromacs User Manual version 4.5.4. Available at: www.gromacs.org
- Verkhivker, G. M., Agajanian, S., Oztas, D. Y., Gupta, G. (2021). Atomistic Simulations and In Silico Mutational Profiling of Protein Stability and Binding in the SARS-CoV-2 Spike Protein Complexes with Nanobodies: Molecular Determinants of Mutational Escape Mechanisms. ACS Omega, 6 (40), 26354–26371. doi: https://doi.org/10.1021/acsomega.1c03558
- Domingo, M., Faraudo, J. (2023). Effect of surfactants on SARS-CoV-2: Molecular dynamics simulations. The Journal of Chemical Physics, 158 (11). doi: https://doi.org/10.1063/5.0135251
- Samanta, P. N., Majumdar, D., Leszczynski, J. (2023). Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism. Journal of Chemical Information and Modeling, 63 (11), 3404–3422. doi: https://doi.org/10.1021/acs.jcim.3c00105
- Rashid, H. ur, Ahmad, N., Abdalla, M., Khan, K., Martines, M. A. U., Shabana, S. (2022). Molecular docking and dynamic simulations of Cefixime, Etoposide and Nebrodenside A against the pathogenic proteins of SARS-CoV-2. Journal of Molecular Structure, 1247, 131296. doi: https://doi.org/10.1016/j.molstruc.2021.131296
- Muhseen, Z. T., Hameed, A. R., Al-Hasani, H. M. H., Tahir ul Qamar, M., Li, G. (2020). Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach. Journal of Molecular Liquids, 320, 114493. doi: https://doi.org/10.1016/j.molliq.2020.114493
- Wakchaure, P. D., Ghosh, S., Ganguly, B. (2020). Revealing the Inhibition Mechanism of RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2 by Remdesivir and Nucleotide Analogues: A Molecular Dynamics Simulation Study. The Journal of Physical Chemistry B, 124 (47), 10641–10652. doi: https://doi.org/10.1021/acs.jpcb.0c06747
- Byléhn, F., Menéndez, C. A., Perez-Lemus, G. R., Alvarado, W., de Pablo, J. J. (2021). Modeling the Binding Mechanism of Remdesivir, Favilavir, and Ribavirin to SARS-CoV-2 RNA-Dependent RNA Polymerase. ACS Central Science, 7 (1), 164–174. doi: https://doi.org/10.1021/acscentsci.0c01242
- Ghorbani, M., Brooks, B. R., Klauda, J. B. (2020). Critical Sequence Hotspots for Binding of Novel Coronavirus to Angiotensin Converter Enzyme as Evaluated by Molecular Simulations. The Journal of Physical Chemistry B, 124 (45), 10034–10047. doi: https://doi.org/10.1021/acs.jpcb.0c05994
- Carli, M., Sormani, G., Rodriguez, A., Laio, A. (2020). Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations. The Journal of Physical Chemistry Letters, 12 (1), 65–72. doi: https://doi.org/10.1021/acs.jpclett.0c03182
- Sakkiah, S., Guo, W., Pan, B., Ji, Z., Yavas, G., Azevedo, M. et al. (2021). Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations. Frontiers in Chemistry, 8, 622632. doi: https://doi.org/10.3389/fchem.2020.622632
- Sheik Amamuddy, O., Verkhivker, G. M., Tastan Bishop, Ö. (2020). Impact of Early Pandemic Stage Mutations on Molecular Dynamics of SARS-CoV-2 Mpro. Journal of Chemical Information and Modeling, 60 (10), 5080–5102. doi: https://doi.org/10.1021/acs.jcim.0c00634
- Rajagopal, K., Varakumar, P., Aparna, B., Byran, G., Jupudi, S. (2020). Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies. Journal of Biomolecular Structure and Dynamics, 39 (15), 5551–5562. doi: https://doi.org/10.1080/07391102.2020.1798285
- Yoshino, R., Yasuo, N., Sekijima, M. (2020). Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Scientific Reports, 10 (1), 12493. doi: https://doi.org/10.1038/s41598-020-69337-9
- Sang, P., Tian, S.-H., Meng, Z.-H., Yang, L.-Q. (2020). Anti-HIV drug repurposing against SARS-CoV-2. RSC Advances, 10 (27), 15775–15783. doi: https://doi.org/10.1039/d0ra01899f
- Mukherjee, S., Dasgupta, S., Adhikary, T., Adhikari, U., Panja, S. S. (2020). Structural insight to hydroxychloroquine-3C-like proteinase complexation from SARS-CoV-2: inhibitor modelling study through molecular docking and MD-simulation study. Journal of Biomolecular Structure and Dynamics, 39 (18), 7322–7334. doi: https://doi.org/10.1080/07391102.2020.1804458
- Grottesi, A., Bešker, N., Emerson, A., Manelfi, C., Beccari, A. R., Frigerio, F. et al. (2020). Computational Studies of SARS-CoV-2 3CLpro: Insights from MD Simulations. International Journal of Molecular Sciences, 21 (15), 5346. doi: https://doi.org/10.3390/ijms21155346
- Kumar, D., Kumari, K., Jayaraj, A., Kumar, V., Kumar, R. V., Dass, S. K. et al. (2020). Understanding the binding affinity of noscapines with protease of SARS-CoV-2 for COVID-19 using MD simulations at different temperatures. Journal of Biomolecular Structure and Dynamics, 39 (7), 2659–2672. doi: https://doi.org/10.1080/07391102.2020.1752310
- Al-Khafaji, K., AL-Duhaidahawi, D., Taskin Tok, T. (2021). Using integrated computational approaches to identify safe and rapid treatment for SARS-CoV-2. Journal of Biomolecular Structure and Dynamics, 39 (9), 3387–3395. doi: https://doi.org/10.1080/07391102.2020.1764392
- Muhammad, I. A., Muangchoo, K., Muhammad, A., Ajingi, Y. S., Muhammad, I. Y., Umar, I. D., Muhammad, A. B. (2020). A Computational Study to Identify Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro) from Eucalyptus Active Compounds. Computation, 8 (3), 79. doi: https://doi.org/10.3390/computation8030079
- Maurya, A. K., Mishra, N. (2020). In silico validation of coumarin derivatives as potential inhibitors against Main Protease, NSP10/NSP16-Methyltransferase, Phosphatase and Endoribonuclease of SARS CoV-2. Journal of Biomolecular Structure and Dynamics, 39 (18), 7306–7321. doi: https://doi.org/10.1080/07391102.2020.1808075
- Alamri, M. A., Tahir ul Qamar, M., Mirza, M. U., Bhadane, R., Alqahtani, S. M., Muneer, I. et al. (2020). Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro. Journal of Biomolecular Structure and Dynamics, 39 (13), 4936–4948. doi: https://doi.org/10.1080/07391102.2020.1782768
- Stoddard, S. V., Stoddard, S. D., Oelkers, B. K., Fitts, K., Whalum, K., Whalum, K. et al. (2020). Optimization Rules for SARS-CoV-2 Mpro Antivirals: Ensemble Docking and Exploration of the Coronavirus Protease Active Site. Viruses, 12 (9), 942. doi: https://doi.org/10.3390/v12090942
- Rout, J., Swain, B. C., Tripathy, U. (2020). In silicoinvestigation of spice molecules as potent inhibitor of SARS-CoV-2. Journal of Biomolecular Structure and Dynamics, 40 (2), 860–874. doi: https://doi.org/10.1080/07391102.2020.1819879
- Umesh, Kundu, D., Selvaraj, C., Singh, S. K., Dubey, V. K. (2021). Identification of new anti-nCoV drug chemical compounds from Indian spices exploiting SARS-CoV-2 main protease as target. Journal of Biomolecular Structure and Dynamics, 39 (9), 3428–3434. doi: https://doi.org/10.1080/07391102.2020.1763202
- El Hassab, M. A., Shoun, A. A., Al-Rashood, S. T., Al-Warhi, T., Eldehna, W. M. (2020). Identification of a New Potential SARS-COV-2 RNA-Dependent RNA Polymerase Inhibitor via Combining Fragment-Based Drug Design, Docking, Molecular Dynamics, and MM-PBSA Calculations. Frontiers in Chemistry, 8, 584894. doi: https://doi.org/10.3389/fchem.2020.584894
- Gangadevi, S., Badavath, V. N., Thakur, A., Yin, N., De Jonghe, S., Acevedo, O. et al. (2021). Kobophenol A Inhibits Binding of Host ACE2 Receptor with Spike RBD Domain of SARS-CoV-2, a Lead Compound for Blocking COVID-19. The Journal of Physical Chemistry Letters, 12 (7), 1793–1802. doi: https://doi.org/10.1021/acs.jpclett.0c03119
- Liang, J., Karagiannis, C., Pitsillou, E., Darmawan, K. K., Ng, K., Hung, A., Karagiannis, T. C. (2020). Site mapping and small molecule blind docking reveal a possible target site on the SARS-CoV-2 main protease dimer interface. Computational Biology and Chemistry, 89, 107372. doi: https://doi.org/10.1016/j.compbiolchem.2020.107372
- Khan, M. A., Meena, S., Alam, Md. A., Ghosh, S. (2020). A solvent sensitive coumarin derivative coupled with gold nanoparticles as selective fluorescent sensor for Pb2+ ions in real samples. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 243, 118810. doi: https://doi.org/10.1016/j.saa.2020.118810
- Kumar, K. A., Sharma, M., Dalal, V., Singh, V., Tomar, S., Kumar, P. (2021). Multifunctional inhibitors of SARS-CoV-2 by MM/PBSA, essential dynamics, and molecular dynamic investigations. Journal of Molecular Graphics and Modelling, 107, 107969. doi: https://doi.org/10.1016/j.jmgm.2021.107969
- Eltayb, W. A., Abdalla, M., Rabie, A. M. (2023). Novel Investigational Anti-SARS-CoV-2 Agent Ensitrelvir “S-217622”: A Very Promising Potential Universal Broad-Spectrum Antiviral at the Therapeutic Frontline of Coronavirus Species. ACS Omega, 8 (6), 5234–5246. doi: https://doi.org/10.1021/acsomega.2c03881
- King, E., Aitchison, E., Li, H., Luo, R. (2021). Recent Developments in Free Energy Calculations for Drug Discovery. Frontiers in Molecular Biosciences, 8, 712085. doi: https://doi.org/10.3389/fmolb.2021.712085
- Zara, L., Efrém, N.-L., van Muijlwijk-Koezen, J. E., de Esch, I. J. P., Zarzycka, B. (2021). Progress in Free Energy Perturbation: Options for Evolving Fragments. Drug Discovery Today: Technologies, 40, 36–42. doi: https://doi.org/10.1016/j.ddtec.2021.10.001
- Hansson, T., Marelius, J., Åqvist, J. (1998). Ligand binding affinity prediction by linear interaction energy methods. Journal of Computer-Aided Molecular Design, 12 (1), 27–35. doi: https://doi.org/10.1023/a:1007930623000
- Ngo, S. T., Hung, H. M., Nguyen, M. T. (2016). Fast and accurate determination of the relative binding affinities of small compounds to HIV-1 protease using non-equilibrium work. Journal of Computational Chemistry, 37 (31), 2734–2742. doi: https://doi.org/10.1002/jcc.24502
- Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L. et al. (2000). Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accounts of Chemical Research, 33 (12), 889–897. doi: https://doi.org/10.1021/ar000033j
- Jarzynski, C. (1997). Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach. Physical Review E, 56 (5), 5018–5035. doi: https://doi.org/10.1103/physreve.56.5018
- Kollman, P. (1993). Free energy calculations: Applications to chemical and biochemical phenomena. Chemical Reviews, 93 (7), 2395–2417. doi: https://doi.org/10.1021/cr00023a004
- Zwanzig, R. W. (2004). High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases. The Journal of Chemical Physics, 22 (8), 1420–1426. doi: https://doi.org/10.1063/1.1740409
- Fu, H., Chen, H., Cai, W., Shao, X., Chipot, C. (2021). BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations. Journal of Chemical Information and Modeling, 61 (5), 2116–2123. doi: https://doi.org/10.1021/acs.jcim.1c00269
- Blazhynska, M., Goulard Coderc de Lacam, E., Chen, H., Roux, B., Chipot, C. (2022). Hazardous Shortcuts in Standard Binding Free Energy Calculations. The Journal of Physical Chemistry Letters, 13 (27), 6250–6258. doi: https://doi.org/10.1021/acs.jpclett.2c01490
- Coderc de Lacam, E. G., Blazhynska, M., Chen, H., Gumbart, J. C., Chipot, C. (2022). When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy. Journal of Chemical Theory and Computation, 18 (10), 5890–5900. doi: https://doi.org/10.1021/acs.jctc.2c00604
- Gupta, A., Zhou, H.-X. (2020). Profiling SARS-CoV-2 Main Protease (MPRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields. ACS Combinatorial Science, 22 (12), 826–832. doi: https://doi.org/10.1021/acscombsci.0c00140
- Ngo, S. T., Nguyen, T. H., Tung, N. T., Mai, B. K. (2022). Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro. RSC Advances, 12 (6), 3729–3737. doi: https://doi.org/10.1039/d1ra08752e
- Zanetti-Polzi, L., Smith, M. D., Chipot, C., Gumbart, J. C., Lynch, D. L., Pavlova, A. et al. (2021). Tuning Proton Transfer Thermodynamics in SARS-CoV-2 Main Protease: Implications for Catalysis and Inhibitor Design. The Journal of Physical Chemistry Letters, 12 (17), 4195–4202. doi: https://doi.org/10.1021/acs.jpclett.1c00425
- Qayed, W. S., Ferreira, R. S., Silva, J. R. A. (2022). In Silico Study towards Repositioning of FDA-Approved Drug Candidates for Anticoronaviral Therapy: Molecular Docking, Molecular Dynamics and Binding Free Energy Calculations. Molecules, 27 (18), 5988. doi: https://doi.org/10.3390/molecules27185988
- Parks, C. D., Gaieb, Z., Chiu, M., Yang, H., Shao, C., Walters, W. P. et al. (2020). D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies. Journal of Computer-Aided Molecular Design, 34 (2), 99–119. doi: https://doi.org/10.1007/s10822-020-00289-y
- Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F. et al. (2022). Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nature Protocols, 17 (3), 672–697. doi: https://doi.org/10.1038/s41596-021-00659-2
- Gorgulla, C., Boeszoermenyi, A., Wang, Z.-F., Fischer, P. D., Coote, P. W., Padmanabha Das, K. M. et al. (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature, 580 (7805), 663–668. doi: https://doi.org/10.1038/s41586-020-2117-z
- Arantes, P. R., Saha, A., Palermo, G. (2020). Fighting COVID-19 Using Molecular Dynamics Simulations. ACS Central Science, 6 (10), 1654–1656. doi: https://doi.org/10.1021/acscentsci.0c01236
- Frye, L., Bhat, S., Akinsanya, K., Abel, R. (2021). From computer-aided drug discovery to computer-driven drug discovery. Drug Discovery Today: Technologies, 39, 111–117. doi: https://doi.org/10.1016/j.ddtec.2021.08.001
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