Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

Sarfaraz K. Niazi , Zamara Mariam

Research output: Contribution to journalReview articlepeer-review

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Abstract

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.
Original languageEnglish
Article number22
Number of pages1
JournalPharmaceuticals
Volume17
Issue number1
Early online date22 Dec 2023
DOIs
Publication statusPublished - 1 Jan 2024

Bibliographical note

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords

  • Computer-Aided Drug Design (CADD)
  • Machine Learning and Artificial Intelligence (AI)
  • Molecular Medicine
  • drug discovery
  • Chemoinformatics
  • molecular modeling
  • molecular docking
  • target identification

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