Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition.

Autor: Bharadwaj S; Center for SeNSE, Indian Institute of Technology Delhi (IIT), New Delhi, India., Deepika K; Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India., Kumar A; Amity Institute of Pharmacy (AIP), Amity University Haryana, Manesar, India., Jaiswal S; Institute of Pharmaceutical Research, GLA University, Mathura, India., Miglani S; Department of Education, Central University of Punjab, Bathinda, India., Singh D; IES Institute of Pharmacy, IES University, Bhopal, Madhya Pradesh, India., Fartyal P; Department of Mathematics, Govt PG College Bajpur (US Nagar), Bazpur, Uttarakhand, India., Kumar R; Department of Microbiology, Graphic Era (Deemed to be University), Dehradun, India.; Department of Microbiology, Central University of Punjab, VPO-Ghudda, Punjab, India., Singh S; Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India., Singh MP; Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India., Gaidhane AM; Jawaharlal Nehru Medical College, and Global Health Academy, School of Epidemiology and Public Health, Datta Meghe Institute of Higher Education, Wardha, India., Kumar B; Department of Pharmaceutical Science, Hemvati Nandan Bahuguna Garhwal (A Central) University, Srinagar, Uttarakhand, India., Jha V; Institute of Cancer Therapeutics, School of Pharmacy and Medical Sciences, Faculty of Life Sciences, University of Bradford, Bradford, UK.
Jazyk: angličtina
Zdroj: Chemical biology & drug design [Chem Biol Drug Des] 2024 Oct; Vol. 104 (4), pp. e14639.
DOI: 10.1111/cbdd.14639
Abstrakt: The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.
(© 2024 The Author(s). Chemical Biology & Drug Design published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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