Affiliation:
1. Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education
for Women, Coimbatore, Tamil Nadu, India
Abstract
Abstract:
It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly
time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial
intelligence (AI) have helped to overcome the challenges in the drug discovery pipeline. Artificial intelligence
(AI) has taken root in various pharmaceutical sectors, from lead compound identification to clinical trials.
Deep learning (DL) is a component of artificial intelligence (AI) that has excelled in many fields of Artificial
intelligence (AI) research over the past decades. Its numerous applications in the realms of science and technology,
especially in biomedicine and bioinformatics, are witnessed deep learning (DL) applications significantly
accelerate drug discovery and pharmaceutical research in recent years, and their usefulness has exceeded expectations
and shown good promise in tackling a range of issues with drug discovery. Deep learning (DL) holds
great potential for drug development since it allows for sophisticated image interpretation, molecular structure
and function prediction, and the automated creation of novel chemical entities with specific features. In the process
of drug discovery, deep learning (DL) can be incorporated at all stages like identification of targets, prognostic
biomarkers, drug designing and development, synergism and antagonism prediction, etc. This review
summarizes various approaches of deep learning (DL) in drug discovery like deep generative models for drug
discovery, deep learning (DL) tools for drug discovery, synergy prediction, and precision medicine.
Publisher
Bentham Science Publishers Ltd.
Subject
Drug Discovery,Pharmacology
Cited by
1 articles.
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