Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our u...

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Published inBiomolecules (Basel, Switzerland) Vol. 14; no. 3; p. 339
Main Authors Qiu, Xinru, Li, Han, Ver Steeg, Greg, Godzik, Adam
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.03.2024
MDPI
Subjects
Algorithms
AlphaFold2
Analysis
Antineoplastic Agents - pharmacology
Antineoplastic Agents - therapeutic use
Artificial Intelligence
Bioinformatics
Biology
Cancer
Datasets
Disease
Drug Development
Drug Discovery
Drugs
Experimental methods
generative AI
Health aspects
Humans
Kinases
Medical research
Methods
Mutation
Neoplasms - drug therapy
Neural networks
NMR
Nuclear magnetic resonance
Predictions
Protein structure
Protein structure prediction
Proteins
R&D
Research & development
Research methodology
Review
Structure-function relationships
Therapeutic targets
United States
cancer
drug discovery
generative AI
artificial intelligence
AlphaFold2
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Summary:Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.
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ISSN:2218-273X
2218-273X
DOI:10.3390/biom14030339