A Review on Computer Aided Drug Design – In Silico
DOI:
https://doi.org/10.22270/ajprd.v12i6.1467Abstract
The process of drug discovery takes a long time and costs a lot of money. Computer-Aided Drug Design (CADD) has become an important part of modern drug discovery because it speeds up the process and lowers prices. CADD includes many methods, such as Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD). These use computer programs to do things like molecular docking, virtual screening, QSAR, pharmacophore modeling, and molecular dynamics. LBDD is used when the shapes of receptors are unknown, while SBDD uses machine learning. This review provides a comprehensive overview of CADD methods, classification, principles, and uses in drug creation. The article discusses about how important it is to find targets, find lead compounds, and make things work better. It also talks about the role of computers in pharmaceutical chemistry and molecular biology. CADD has increased the speed and accuracy of drug finding, making it possible to find new medicines. The review shows how CADD could change the way drugs are made, help people who don't have access to proper medical care, and make patient results better. Researchers can speed up the process of finding new drugs by using CADD strategies. This review is a great resource for researchers, clinicians, and industry workers who want to use CADD in pharmaceutical research. Using CADD has changed the way drugs are found, and its continued growth could lead to better health for everyone.
Downloads
References
Martin L, Hutchens M, Hawkins C. Clinical trial cycle times continue to increase despite industry efforts. Nat. Rev. Drug Discov. 2017; 16: 157.
Simoens S, Huys I. R&D Costs of New Medicines: A Landscape Analysis. Front. Med. 2021; 8: 760762.
Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, Liu X. A compact review of progress and prospects of deep learning in drug discovery. J Mol Model. 2023;29(4):117. doi: 10.1007/s00894-023-05492-w. PMID: 36976427.
Sydow D, Morger A, Driller M, et al. TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data. J Cheminform. 2019; 11(29): 1-7. https://doi.org/10.1186/s13321-019-0351-x
Sharma A, Jangid L, Shaikh N and Bhangale J. Computer-Aided Drug Design Boon in Drug Discovery. Asian Journal of Organic & Medicinal Chemistry.2022;7(1):55-64
Gu R, Wu F, Huang Z. Role of Computer-Aided Drug Design in Drug Development. Molecules. 2023;28(20):7160. doi: 10.3390/molecules 28207160. PMID: 37894639; PMCID: PMC10609497.
Zhang X, Wu F, Yang N. et al. In silico Methods for Identification of Potential Therapeutic Targets. InterdiscipSci Comput Life Sci 14, 285–310 (2022). https://doi.org/10.1007/s12539-021-00491-y
Gao D, Chen Q, Zeng Y, Jiang M, Zhang Y. Applications of Machine Learning in Drug Target Discovery. Curr Drug Metab. 2020;21(10):790-803. doi: 10.2174/1567201817999200728142023. PMID: 32723266.
Santos Nascimento DJI, Aquino MT de and Silva-Júnior FE da. The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD).Letters in Drug Design & Discovery. 2022(19)951-955.
Medina-Franco JL. Grand Challenges of Computer-Aided Drug Design: The Road Ahead. Front. Drug. Discov. 1:728551. doi: 10.3389/fddsv.2021.72855
Ece A. Computer-aided drug design (2023). 17:26 https://doi.org/10.1186/s13065-023-00939-w
Bajorath J. Deep Machine Learning for Computer-Aided Drug Design. Front. Drug. Discov. 2:829043. doi: 10.3389/fddsv.2022.829043
Zhenhui Wu, MScabc, Shupeng Chen, MScb, Yihao Wang, MDc, Fangyang Li, BScc, Huanhua Xu,MDa, Maoxing Li, MDc, Yingjian Zeng, PhDb, Zhenfeng Wu, PhDa, Yue Gao, PhD ac, Current perspectives and trend of Computer-Aided drug design: a review and bibliometric International Journal of Surgery (2024) Published online19 March 2024.
http://dx.doi.org/10.1097/JS9.0000000000001289
BadanoA. In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable. Trials 22, 64 (2021). https://doi.org/10.1186/s13063-020-05002-w
Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics. 2022 Dec 23;15(1):49. doi: 10.3390/pharmaceutics15010049. PMID: 36678678; PMCID: PMC9867171.
Blundell TL, Jhoti H, Abell C. High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov. 2002 Jan;1(1):45-54. doi: 10.1038/nrd706. PMID: 12119609.
Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules. 2023; 28(9):3906. https://doi.org/10.3390/molecules28093906
Turzo SBA, Hantz ER, Lindert S. Applications of machine learning in computer-aided drug discovery. QRB Discov. 2022;3:e14. doi: 10.1017/qrd.2022.12. PMID: 37529294; PMCID: PMC10392679.
Purohit1D,MakhijaM,Pandey P,KumarS,Kumar S,DuttR,Kaushik D, Kumar P, Kumar S. Role of computer-aided drug design in the discovery and development of new medicinal agents. A review Journal of medical pharmaceutical and alliedsciences.11(3):4794 - 4801 doi: 10.55522/jmpas.V11I3.2300.
Macalino SJ, Gosu V, Hong S, Choi S. Role of computer-aided drug design in modern drug discovery. Arch Pharm Res. 2015;38(9):1686-701. doi: 10.1007/s12272-015-0640-5. Epub 2015 Jul 25. PMID: 26208641.
Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19. Biomed Res Int. 2021 Jun 24;2021:8853056. doi: 10.1155/2021/8853056. PMID: 34258282; PMCID: PMC8241505
Sharma1 V, Wakode1 S, Kumar H. A review on Structure- and Ligand-based drugdesign:Concepts, approaches, And challenges ,Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences, 2021;https://doi.org/10.1016/B978-0-12-821748-1.00004-X
Das PS and Saha P. A Review On Computer Aided Drug Design In Drug Discovery,World Journal of Pharmacy and Pharmaceutical Sciences. 2017; 6(7):279-291. DOI: 10.20959/wjpps 20177-9450
QidwaiT. QSAR modeling, docking and ADMET studies for exploration of potential anti-malarial compounds against Plasmodium falciparum. In Silico Pharmacol. 5, 6 (2017).
https://doi.org/10.1007/s40203-017-0026-0
Malathi K, Ramaiah S. Bioinformatics approaches for new drug discovery: a review. Biotechnology and Genetic Engineering Reviews. 34(2):243–260. https://doi.org/10.1080/02648725.2018.1502984
Elalouf A. In-silico Structural Modeling of Human Immunodeficiency Virus Proteins. Biomed Eng Comput Biol. 2023;14:11795972231154402. doi: 10.1177/11795972231154402. PMID: 36819710; PMCID: PMC9936402.
Salman MM, Al-Obaidi Z, Kitchen P, Loreto A, Bill RM, Wade-Martins R. Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases. International Journal of Molecular Sciences. 2021; 22(9):4688. https://doi.org/10.3390/ijms22094688
Dorahy G, Chen JZ, Balle T. Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs. Molecules. 2023;28(3):1324. doi:10.3390/molecules28031324. PMID: 36770990; PMCID: PMC9921936.
Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem. 2020;8:343. doi:10.3389/fchem.2020.00343. PMID: 32411671; PMCID: PMC7200080.
Oliveira TAd, Silva MPd, Maia EHB, Silva AMd, Taranto AG. Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods. Drugs and Drug Candidates. 2023; 2(2):311-334. https://doi.org/10.3390/ddc2020017
Dar KB, Bhat AH, Amin S, Hamid R, Anees S, Anjum S, Reshi BA, Zargar MA, Masood A, Ganie SA. Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect. Curr Top Med Chem. 2018;18(31):2702-2719. doi:10.2174/1568026619666190119150741. PMID: 30659543.
Chandershekar A, Bhaskar A,Mekkanti MR,Mathappan R. A review On Computer Aided Drug Design (CAAD) and It’s Implications. In Drug Discovery And Development Process International Journal of Health Care and Biological Sciences. 2020; 1(1):27-33.
Sahu A, Pradhan D, Veer B, Kumar S, Singh R, Raza K, Rizvi MA, Jain AK, Verma S. In silico screening, synthesis, characterization and biological evaluation of novel anticancer agents as potential COX-2 inhibitors. Daru. 2023; 31(2):119-133. doi:10.1007/s40199-023-00467-x. Epub 2023 Jul 15. PMID: 37454036; PMCID: PMC10624798.
Raval K, Ganatra T. Basics, types and applications of molecular docking: A review. IP Int J Compr Adv Pharmacol. 2022;7(1):12-16.https://doi.org/10.18231/j.ijcaap.2022.0032581-5555/©2022
Dana D, Gadhiya SV, St Surin LG, Li D, Naaz F, Ali Q, Paka L, Yamin MA, Narayan M, Goldberg ID, Narayan P. Deep Learning in Drug Discovery and Medicine; Scratching the Surface. Molecules. 2018; 23(9):2384.doi:10.3390/molecules23092384. PMID: 30231499; PMCID: PMC6225282.
Wadood, Abdul et al. In-silico drug design: An approach which revolutionarised the drug discovery process. Drug design and delivery. (2013): DOI:10.13172/2054-4057-1-1-1119 Corpus ID: 5021463
Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL,Egieyeh ES,Sibuyi NRS,MeyerM,Madiehe AM,Wyckoffe GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the Discovery of novel antimicrobial, Computers in Biology and Medicine 2024;169:107927.https://doi.org/10.1016/j.compbiomed.2024.107927
Rim KT. 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. Epub 2020 May 14. PMID: 32421081; PMCID: PMC7223298.
Niazi SK, Mariam Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals (Basel). 2023;17(1):22. doi:10.3390/ph17010022. PMID: 38256856; PMCID: PMC10819513.
Cerchia C, Lavecchia A. In Silico Drug Design and Discovery: Big Data for Small Molecule Design. Biomolecules. 2022; 13(1):44. doi:10.3390/biom13010044. PMID: 36671429; PMCID: PMC9855915.
Tiwari A, Singh S.Computational approaches in drug designing. Bioinformatics: Methods and Applications. 2021:207-217.https://doi.org/10.1016/B978-0-323-89775-4.00010-9
Buglak AA,Samokhvalov AV, Zherdev AV, Dzantiev BB. Methods and Applications of In Silico Aptamer Design and Modeling. International Journal of Molecular Sciences.2020; 21(22):8420. https://doi.org/10.3390/ijms21228420
Zohora FT, Azam ATMZ, Ahmed S, Rahman KM, Halim MA, Anwar MR, Sohrab MH, Tabassum F, Hasan CM, Ahsan M. Isolation and In Silico Prediction of Potential Drug-like Compounds with a New Dimeric Prenylated Quinolone Alkaloid from Zanthoxylum rhetsa (Roxb.) Root Extracts Targeted against SARS-CoV-2 (Mpro). Molecules. 2022; 27(23):8191.
https://doi.org/10.3390/molecules27238191
Sydow D,Rodr´ıguez-GuerraJ,Kimber TB,SchallerD,Taylor CJ,Chen Y,LejaM,Misra S,Wichmann M, Ariamajd A and Volkamer A. Teach Open CADD, open source and FAIR Python Pipelines to assist in structural bioinformatics and Cheminformatics, research Nucleic Acids Research. 2022; 50:https://doi.org/10.1093/nar/gkac267
Wang Y, Xing J, Xu Y, Zhou N, Peng J, Xiong Z, Liu X, Luo X, Luo C, Chen K, Zheng M, Jiang H. In silico ADME/T modelling for rational drug design. Q Rev Biophys. 2015;48(4):488-515. doi: 10.1017/S0033583515000190. Epub 2015 Sep 2. PMID: 26328949.
Onyango OH. In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review, Advances in Pharmacological and Pharmaceutical Sciences. 2023:https://doi.org/10.1155/2023/4562974
Naz S, Aroosh A, Caner A, Şahar EA, Toz S, Ozbel Y, Abbasi SW. Immunoinformatics Approach to Design a Multi-Epitope Vaccine against Cutaneous Leishmaniasis. Vaccines (Basel). 2023;11(2):339. doi: 10.3390/vaccines11020339. PMID: 36851219; PMCID: PMC9967539.
Vicente-Barrueco A, Román ÁC, Ruiz-Téllez T, Centeno F. In Silico Research of New Therapeutics Rotenoids Derivatives against Leishmania amazonensis Infection. Biology (Basel). 2022 Jan 14;11(1):133. doi: 10.3390/biology11010133. PMID: 35053132; PMCID: PMC8772715.
Sahu A, Ahmad S, Imtiyaz K. et al. In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma. Sci Rep 13, 16545 (2023). https://doi.org/10.1038/s41598-023-43789-1
Cavalcante BRR, Freitas RD, Siquara da Rocha L d O, Santos R d SB D, Souza BSdF, Ramos PIP, Rocha GV and Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front.Pharmacol. 15:1400029. doi: 10.3389/fphar.2024.1400029
Liu M, Lv J, Chen L, Li W, Han W. In Silico Discovery of Anticancer Peptides from Sanghuang. Int J Mol Sci. 2022;23(22):13682. doi: 10.3390/ijms232213682. PMID: 36430160; PMCID: PMC9693127.
Dmitriev AV, Rudik AV, Karasev DA, Pogodin PV, Lagunin AA, Filimonov DA, Poroikov VV. In Silico Prediction of Drug–Drug Interactions Mediated by Cytochrome P450 Isoforms. Pharmaceutics. 2021; 13(4):538. https://doi.org/10.3390/pharmaceutics13040538
Rahman MM, Islam MR, Akash S, Mim SA, Rahaman MS, Emran TB, Akkol EK, Sharma R, Alhumaydhi FA, Sweilam SH, Hossain ME, Ray TK, Sultana S, Ahmed M, Sobarzo-Sánchez E, Wilairatana P. Insilico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective. Front Cell Infect Microbiol. 2022; 12:929430. doi:10.3389/fcimb.2022.929430. PMID: 36072227; PMCID: PMC9441699.
Kaur D, Patiyal S, Arora C, Singh R, Lodhi G, Raghava GPS. In-Silico Tool for Predicting, Scanning, and Designing Defensins. Front Immunol. 2021; 12:780610. doi:10.3389/fimmu.2021.780610. PMID: 34880873; PMCID:PMC8645896.
Published
How to Cite
Issue
Section
Copyright (c) 2024 Pranali R. Bhujade, Khemutai G. Shedame, Pooja R. Hatwar, Dr. Ravindra L. Bakal, Krushnali N. Nehar, Ankita Y.Gawai
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
AUTHORS WHO PUBLISH WITH THIS JOURNAL AGREE TO THE FOLLOWING TERMS:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 Unported License. that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.