The DOI matches its Crossref record; 154 of the 156 references it cites were found in a registry or an index.
Role of computer-aided drug design in modern drug discovery
Abstract
Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.
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“Computer-aided drug design has helped expedite the development of small-molecule inhibitors.172,173”
From Chasing molecular glue degraders: screening approaches (2022) · cites this paper as [172]
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Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 26,294
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M., Smith, J., Kasson, P., van der Spoel, D., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt055 · Cited by 7,255
The price of innovation: new estimates of drug development costs
DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00126-1 · Cited by 4,265
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
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Computational Approaches in Preclinical Studies on Drug Discovery and Development
Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X., Liang, X., Tan, M., & Huang, Z. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00726 · Cited by 494
Chalcone Derivatives: Promising Starting Points for Drug Design
Gomes, M. D. N., Muratov, E. N., Pereira, M., Peixoto, J. D. C., Rosseto, L. P., Cravo, P., Andrade, C. H., & Neves, B. J. (2017) · Molecules · doi:10.3390/molecules22081210 · Cited by 440
Metadata
- Authors
- Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S.
- Affiliations
- Ewha Womans University (KR)
- Corresponding author
- Choi, Sun
- Venue
- Archives of Pharmacal Research
- Published
- 2015-07-25
- Volume
- 38
- Issue
- 9
- Pages
- 1686-1701
- ISSN
- 0253-6269 (ISSN-L)
- 1976-3786
- OpenAlex
- W1854810044
- Cited by
- 762
- References
- 156
- Citations by year
- 2026: 65 · 2025: 80 · 2024: 109 · 2023: 146 · 2022: 105 · 2021: 96 · 2020: 60 · 2019: 41 · 2018: 31 · 2017: 15 · 2016: 10 · 2015: 2
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Archives of Pharmacal Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- The Ministry of Science, ICT & Future Planning (MSIP) and National Research Foundation of Korea (2011-0028885)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology (medical) › Cholinesterase and Neurodegenerative Diseases
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- computer-aided drug design, drug discovery, structure-based drug design, ligand-based drug design, virtual screening, chemical biology, lead optimization, rational drug design
- MeSH terms
- Animals, Pharmaceutical Preparations, Humans, Drug Design, Computer-Aided Design, Protein Structure, Secondary, Protein Structure, Tertiary, Quantitative Structure-Activity Relationship, Drug Discovery
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