Abstract

ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY

*Prof. Ashwini Shrikrushna Taware, Ms. Neha Satish Shinde

Abstract

Artificial Intelligence (AI) has rapidly transformed the landscape of drug development by enabling data-driven, efficient, and predictive approaches across the pharmaceutical pipeline. This review provides a comprehensive overview of AI applications in drug development, highlighting its core functions, advantages over traditional systems, and real-world implementations. The fundamental functions of AI including pattern recognition, predictive modeling, data mining, and decision support facilitate the analysis of complex biological, chemical, and clinical datasets with high accuracy and speed. The review contrasts AI-based approaches with conventional drug development methods, which are often limited by high costs, prolonged timelines, low success rates, and reliance on trial-and-error experimentation. By addressing these drawbacks, AI enhances target identification, lead optimization, and safety profiling while reducing attrition rates. Common examples of AI technologies discussed include machine learning algorithms, deep learning models, natural language processing, and expert systems, all of which contribute to improved computational efficiency and knowledge extraction. Furthermore, the application of AI in drug development is explored in detail, including virtual screening, drug target interaction prediction, de novo drug design, pharmacokinetic and toxicity prediction (ADMET), clinical trial optimization, and drug repurposing. Despite its transformative potential, challenges such as data quality, interpretability, ethical concerns, and regulatory acceptance remain significant. This review concludes that AI represents a paradigm shift in drug development, offering substantial opportunities to enhance innovation, reduce costs, and accelerate the delivery of safe and effective therapeutics.

Keywords: Artificial Intelligence, Drug Development, Functions of Artificial Intelligence, Drawbacks of Traditional System, Common Examples of AI, Discovery and Development.