Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
Abstract
:1. Introduction to Computer-Aided Drug Design (CADD)
Computer-Aided Drug Design (CADD): A Synthesis of Biology and Technology
2. Key Techniques and Approaches in CADD
Delineating the Array of Techniques in Computer-Aided Drug Design
3. Integration of Machine Learning and AI in CADD
3.1. Machine Learning and AI: The New Vanguard in Drug Discovery
3.2. Implications of ML in CADD
4. Challenges and Limitations in CADD
Understanding the Obstacles: The Roadblocks in Computer-Aided Drug Design
5. Experimental Validation in CADD: From In-Silico to the Lab Bench
Bridging Computational Predictions with Reality
6. Harnessing the Power of AI: A Paradigm Shift in Drug Discovery
7. Integration of Multi-Omics Data in CADD
Holistic Viewpoints: Embracing the Complexity of Biology through Multi-Omic Integration
8. Current Challenges in CADD
Overcoming Barriers: The Evolving Landscape of Challenges in Computer-Aided Drug Design
9. Case Studies: Success Stories in CADD
From Concept to Clinic: Triumphs in Computer-Aided Drug Design
10. The Future of CADD: Emerging Technologies and Innovations
10.1. Charting the Horizon: Navigating the Next Frontiers of Computer-Aided Drug Design
10.2. Unity in Diversity: Harnessing Global Intelligence in Computer-Aided Drug Design
10.3. Drawing Lines in the Digital Sand: Navigating the Ethical and Regulatory Labyrinths of Computer-Aided Drug Design
10.4. A Glimpse into the Horizon: Envisioning the Next Epoch of Computer-Aided Drug Design
11. Bridging the Gap: Integrating Experimental Data with CADD
11.1. Forging Synergy: When the Computational Meets the Experimental in Drug Design
11.2. Shaping the Drug Designers of Tomorrow: The Essentiality of CADD in Modern Education
12. The Future Outlook: CADDs Trajectory and Upcoming Challenges
13. Collaborative Efforts and Global Initiatives in CADD
Bridging Boundaries: How Global Collaborations Are Amplifying the Impact of CADD
14. CADD in Personalized Medicine: Tailoring Therapies to Individuals
15. Elevating Drug Design: The Convergence of AI, Machine Learning, and CADD
16. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | Programs |
---|---|
Homology Modeling/Comparative Modeling: Create a 3D model of the target protein using a homologous protein’s empirically confirmed structure as a guide. | MODELLER, SWISS-MODEL, Phyre2, RaptorX, I-TASSER |
Ab Initio Modeling: Build a 3D model of the target protein by sampling the protein’s conformational space without using experimental data. | Rosetta, QUARK, AlphaFold, ESMFold, PCONS5 |
Threading: Build a 3D model of the target protein by aligning the protein sequence with the sequences of proteins of known structure. | MUSTER, 3D-PSSM, LOMETS, HHpred |
Hybrid Modeling: Combine two or more modeling approaches to improve the accuracy of the predicted structure. | CABS-flex, PrimeX, GalaxyHomomer |
Molecular Dynamics: Simulate the behavior of the protein over time using classical or quantum mechanics. | GROMACS, NAMD, CHARMM |
Knowledge-based methods: Use existing knowledge about protein structure and function to predict the structure of the target protein. | ProSMoS, ProQ3D, I-TASSER-2GO |
Template-free methods: Build a 3D model of the target protein without using templates or homologous proteins. | CONFOLD2, MetaPSICOV, TrRosetta |
Fragment-assembly methods: Build a 3D model of the target protein by assembling fragments of known protein structures. | PEP-FOLD3, Robetta, QUARK |
Tool | Application | Advantages | Disadvantages |
---|---|---|---|
AutoDock Vina | Predicting the binding affinities and orientations of ligands. | Fast, accurate, and easy to use. | May not be as accurate for complex systems. |
AutoDock GOLD | Predicting the binding affinities and orientations of ligands, especially for flexible ligands. | Accurate for flexible ligands. | Requires a license and can be expensive. |
Glide | Predicting the binding affinities and orientations of ligands. | Accurate and integrated with other Schrödinger tools. | Requires the Schrödinger suite, which can be expensive. |
DOCK | Predicting the binding affinities and orientations of ligands and performing virtual screening. | It is versatile and can be used for both docking and virtual screening. | Can be slower than other tools. |
LigandFit | Predicting the binding affinities and orientations of ligands. | Easy to use and integrated with other Schrödinger tools. | May not be as accurate for complex systems. |
SwissDock | Predicting the binding affinities and orientations of ligands. | Easy to use and accessible online. | May not be as accurate for complex systems. |
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Niazi, S.K.; Mariam, Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals 2024, 17, 22. https://doi.org/10.3390/ph17010022
Niazi SK, Mariam Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals. 2024; 17(1):22. https://doi.org/10.3390/ph17010022
Chicago/Turabian StyleNiazi, Sarfaraz K., and Zamara Mariam. 2024. "Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis" Pharmaceuticals 17, no. 1: 22. https://doi.org/10.3390/ph17010022