Review
Version 1
Preserved in Portico This version is not peer-reviewed
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
Version 1
: Received: 13 November 2023 / Approved: 14 November 2023 / Online: 14 November 2023 (16:54:42 CET)
A peer-reviewed article of this Preprint also exists.
Niazi, S.K.; Mariam, Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals 2024, 17, 22. Niazi, S.K.; Mariam, Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals 2024, 17, 22.
Abstract
In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's 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 CADD's 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. The 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.
Keywords
Computer-Aided Drug Design (CADD); Machine Learning and Artificial Intelligence (AI); Drug discovery; Chemoinformatics; Molecular modeling; Molecular Docking; Target identification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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