AI-Based Drug Design: Revolutionizing Drug Discovery through in Silico Analysis

Yamini Priya, Deepthimahanthi and Prakash, Balu and Wong, Ling Shing and Kumar, Krishnan and J., Manjunathan and K., Ashokkumar and M., Jayanthi and M., Suganthi and G., Abirami (2023) AI-Based Drug Design: Revolutionizing Drug Discovery through in Silico Analysis. INTI JOURNAL, 2023 (56). pp. 1-6. ISSN e2600-7320

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Abstract

The convergence of artificial intelligence (AI) and drug design has catalyzed a tectonic shift within the pharmaceutical arena, illuminating a new path towards rapid and efficient drug discovery. This review embarks on an odyssey through the transformative landscape of AI-based drug design, delving into its multifaceted applications and profound implications. By harnessing AI's virtuosity, drug discovery processes are imbued with unprecedented speed and precision. Machine learning algorithms harmonize with intricate biological datasets, unraveling patterns and relationships previously enshrouded in complexity. Deep learning models, akin to modern-day alchemists, sculpt molecular structures and decode binding affinities, accelerating the quest for viable drug candidates. The symphony of AI resonates across the stages of drug discovery, from in silico screening to the hallowed realm of de novo drug design. Virtual libraries become a realm of possibility as AI orchestrates the virtual ballet of compound screening, whittling down the ensemble to a chorus of promising candidates. Moreover, AI's creative fervor burgeons in the crucible of de novo design, forging novel molecules with desired properties. The predictive mastery extends to the realm of absorption, distribution, metabolism, excretion, and toxicity (ADMET) modeling, where AI's crystal ball reveals the fate of molecules based on their molecular signatures. The harmonious confluence of AI and drug design unfolds as a symphony of innovation, orchestrating the metamorphosis of drug discovery into an elegant and efficacious masterpiece

Item Type:	Article
Uncontrolled Keywords:	AI, Deep Learning, Machine learning, ADMET, Drug design, Biological Data
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) R Medicine > RS Pharmacy and materia medica
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	08 Nov 2023 04:06
Last Modified:	08 Nov 2023 04:06
URI:	http://eprints.intimal.edu.my/id/eprint/1812

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