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...

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Vydané v:Pharmaceuticals (Basel, Switzerland) Ročník 17; číslo 1; s. 22
Hlavní autori: Niazi, Sarfaraz K., Mariam, Zamara
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 22.12.2023
MDPI
Predmet:
Algorithms
Artificial intelligence
Biology
Chemoinformatics
Computer simulation
Computer-Aided Drug Design (CADD)
Drug discovery
Hydrogen bonds
Kinases
Ligands
Machine learning
Machine Learning and Artificial Intelligence (AI)
Metabolism
molecular docking
molecular modeling
Pharmaceutical industry
Pharmacokinetics
Proteins
R&D
Research & development
Review
Germany
Computer-Aided Drug Design (CADD)
molecular docking
drug discovery
molecular modeling
target identification
Chemoinformatics
Machine Learning and Artificial Intelligence (AI)
ISSN:1424-8247, 1424-8247
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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.
AbstractList 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.
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.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.
Audience Academic
Author Mariam, Zamara
Niazi, Sarfaraz K.
AuthorAffiliation 1 College of Pharmacy, University of Illinois, Chicago, IL 60012, USA
2 Centre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UK
AuthorAffiliation_xml – name: 2 Centre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UK
– name: 1 College of Pharmacy, University of Illinois, Chicago, IL 60012, USA
Author_xml – sequence: 1
  givenname: Sarfaraz K.
  orcidid: 0000-0002-0513-0336
  surname: Niazi
  fullname: Niazi, Sarfaraz K.
– sequence: 2
  givenname: Zamara
  orcidid: 0000-0002-4563-9559
  surname: Mariam
  fullname: Mariam, Zamara
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38256856$$D View this record in MEDLINE/PubMed
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Issue 1
Keywords Computer-Aided Drug Design (CADD)
molecular docking
drug discovery
molecular modeling
target identification
Chemoinformatics
Machine Learning and Artificial Intelligence (AI)
Language English
License 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/).
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Snippet In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology....
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SubjectTerms Algorithms
Artificial intelligence
Biology
Chemoinformatics
Computer simulation
Computer-Aided Drug Design (CADD)
Drug discovery
Hydrogen bonds
Kinases
Ligands
Machine learning
Machine Learning and Artificial Intelligence (AI)
Metabolism
molecular docking
molecular modeling
Pharmaceutical industry
Pharmacokinetics
Proteins
R&D
Research & development
Review
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Volume 17
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