Abstract

ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY AND DRUG DESIGN: ROLE, RECENT ADVANCES, CURRENT AND FUTURE ROLES, CHALLENGES

Ghrushneshwar Kapde*, Swati Rawat, Pravin Wakte and Sachin Bhusari

ABSTRACT

Artificial intelligence (AI) is transforming drug discovery by significantly improving efficiency and accuracy across various stages of the development pipeline. This review article examines the application of AI techniques, including machine learning, deep learning, and natural language processing, in critical areas such as target identification, compound screening, and lead optimization. We highlight the integration of AI with large-scale biological data and computational tools, which enables better predictions of drug interactions and optimizes molecular design. Notable case studies illustrate successful AI-driven drug discoveries, showcasing the technology's potential to expedite the development process. Additionally, we address challenges such as data quality, algorithm interpretability, and regulatory hurdles that must be navigated to fully harness AI's capabilities. The review also discusses emerging trends and future directions, emphasizing the need for collaborative efforts between academia, industry, and regulatory bodies to establish robust frameworks for AI implementation in drug discovery. Ultimately, this article underscores the promising impact of AI in revolutionizing the pharmaceutical landscape and advancing personalized medicine initiatives.

Keywords: Artificial Intelligence, Drug Discovery, Machine Learning, Deep Learning, Predictive Modeling, De Nova Design.