World Journal of
Pharmaceutical and Life Sciences

DATA INTEGRATION AND ARTIFICIAL INTELLIGENCE: TRANSFORMING NEW DRUG DEVELOPMENT

Shaikh Zainab, Mohd. Arfat*, Shaikh Awais and Syed Ayaz Ali

ABSTRACT

The review signifies and is based on the development of new drug, which is a highly complex and resource-intensive process that has been revolutionized by the integration of artificial intelligence (AI) and data integration techniques. The article focuses on the integration of AI and data promises that is to transform the drug development process, making it more efficient, cost-effective, and tailored to patient needs. As these technologies evolve, they have the potential to deliver innovative therapies that improve patient outcomes and redefine healthcare innovation. Data integration involves harmonizing information from diverse sources, such as genetic, proteomic, chemical, and clinical datasets, to provide a comprehensive understanding of patient characteristics, drug-target interactions, and disease mechanisms. This allows researchers to make informed decisions at every stage of drug development, from target identification to clinical trials. AI, particularly machine learning (ML) and deep learning (DL), plays a pivotal role in analyzing this integrated data. These technologies identify patterns in large-scale datasets, predict drug efficacy, optimize therapeutic strategies, and enhance drug discovery. For example, deep learning models have been used to discover novel compounds like halicin, an antibiotic effective against drug-resistant bacteria. AI also accelerates clinical trials by predicting patient recruitment outcomes and optimizing trial designs. Moreover, AI-driven process optimization improves pharmaceutical manufacturing by ensuring quality control and minimizing production disruptions. Despite these advancements, challenges such as data standardization, and ethical concerns like data privacy remain significant.

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