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Current Drug Delivery

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ISSN (Print): 1567-2018
ISSN (Online): 1875-5704

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Review Article

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Author(s): Vishal Sharma, Amit Singh*, Sanjana Chauhan, Pramod Kumar Sharma, Shubham Chaudhary, Astha Sharma, Omji Porwal and Neeraj Kumar Fuloria

Volume 21, Issue 6, 2024

Published on: 06 September, 2023

Page: [870 - 886] Pages: 17

DOI: 10.2174/1567201821666230905090621

Price: $65

Abstract

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Keywords: Artificial intelligence, machine learning, drug discovery and development, target identification, multi-omics database.

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