Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in ra...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Pharmaceuticals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8247/17/1/22 |
| _version_ | 1797342790278971392 |
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| author | Sarfaraz K. Niazi Zamara Mariam |
| author_facet | Sarfaraz K. Niazi Zamara Mariam |
| author_sort | Sarfaraz K. Niazi |
| collection | DOAJ |
| description | In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery. |
| first_indexed | 2024-03-08T10:38:22Z |
| format | Article |
| id | doaj.art-5b19b93e84184eefbf12d7eff5f6f848 |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-08T10:38:22Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-5b19b93e84184eefbf12d7eff5f6f8482024-01-26T18:04:53ZengMDPI AGPharmaceuticals1424-82472023-12-011712210.3390/ph17010022Computer-Aided Drug Design and Drug Discovery: A Prospective AnalysisSarfaraz K. Niazi0Zamara Mariam1College of Pharmacy, University of Illinois, Chicago, IL 60012, USACentre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UKIn the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.https://www.mdpi.com/1424-8247/17/1/22Computer-Aided Drug Design (CADD)Machine Learning and Artificial Intelligence (AI)drug discoveryChemoinformaticsmolecular modelingmolecular docking |
| spellingShingle | Sarfaraz K. Niazi Zamara Mariam Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis Pharmaceuticals Computer-Aided Drug Design (CADD) Machine Learning and Artificial Intelligence (AI) drug discovery Chemoinformatics molecular modeling molecular docking |
| title | Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis |
| title_full | Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis |
| title_fullStr | Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis |
| title_full_unstemmed | Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis |
| title_short | Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis |
| title_sort | computer aided drug design and drug discovery a prospective analysis |
| topic | Computer-Aided Drug Design (CADD) Machine Learning and Artificial Intelligence (AI) drug discovery Chemoinformatics molecular modeling molecular docking |
| url | https://www.mdpi.com/1424-8247/17/1/22 |
| work_keys_str_mv | AT sarfarazkniazi computeraideddrugdesignanddrugdiscoveryaprospectiveanalysis AT zamaramariam computeraideddrugdesignanddrugdiscoveryaprospectiveanalysis |