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Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect
Khalid Bashir Dar1,2, Aashiq Hussain Bhat1,2, Shajrul Amin1,2
1Department of Clinical Biochemistry, University of Kashmir, Srinagar, 190006, India.
Current Topics in Medicinal Chemistry
|January 20, 2019
Summary
Computer-aided drug design (CADD) accelerates the development of safe and effective therapeutics by reducing the time and cost associated with traditional drug discovery methods. This computational approach aids in identifying potential drug candidates for various diseases.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Pharmacology
Background:
- Traditional drug discovery methods like combinatorial chemistry (CC) and high throughput screening (HTS) are time-consuming and expensive.
- Computer-aided drug design (CADD) offers a more efficient alternative for developing safe and effective drugs.
Purpose of the Study:
- To highlight the indispensable role of CADD in modern drug discovery.
- To showcase the various CADD approaches and their impact on identifying therapeutic compounds.
- To emphasize CADD's contribution to predicting drug properties and accelerating drug development.
Main Methods:
- Pharmacophore modeling (PM)
- Molecular docking (MD)
- Quantitative structure-activity relationship (QSAR)
- Virtual screening (VS)
- Molecular dynamics simulations
Main Results:
- CADD approaches have significantly reduced the time and expenditure compared to traditional methods.
- CADD tools effectively predict the therapeutic outcomes of drug candidates for diseases like cancer, HIV-AIDS, and Ebola.
- Several CADD-derived drugs, including dorzolamide and saquinavir, have successfully passed clinical trials.
Conclusions:
- CADD is a crucial strategy for designing effective drugs and combating human diseases.
- CADD aids in predicting absorption, distribution, metabolism, excretion, and toxicity (ADME/T) of drug candidates.
- Computational approaches have led to the discovery of novel therapeutics, improving patient treatment outcomes.
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