AI-Driven Enhancements in Drug Screening and Optimization.

Autor:	Serghini A; School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia., Portelli S; School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia. s.portelli@uq.edu.au., Ascher DB; School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia. d.ascher@uq.edu.au.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. d.ascher@uq.edu.au.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2714, pp. 269-294.
DOI:	10.1007/978-1-0716-3441-7_15
Abstrakt:	The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline. (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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