Abstract

AI-ENABLED DRUG DISCOVERY: BRIDGING COMPUTATIONAL INNOVATION WITH PHARMACEUTICAL SCIENCES

Gudladona Raghava Ravali*, Purvini K., Swathi Kencha, Savin Sreenath T.P.

ABSTRACT

Finding a new medicine has always been challenging. It often requires many years of work, major financial investment, and numerous laboratory experiments before a drug finally reaches patients. Because of these demands, the process can feel slow and uncertain. In recent years, however, artificial intelligence has begun to noticeably improve this situation. By examining large collections of chemical, biological, and medical information, artificial intelligence helps researchers understand diseases more clearly and identify strong drug candidates much earlier. This review describes how artificial intelligence is now used across the drug discovery process. It explains how computer-based methods analyze complex data, predict the shapes and behavior of important proteins, and help scientists pinpoint where potential medicines may bind. It also highlights how artificial intelligence tools screen vast numbers of chemical compounds, track how molecules might move inside the body, and estimate safety concerns long before testing begins. Newer approaches—such as generative models that propose new chemical structures and tools that reveal how artificial intelligence makes decisions—are also discussed. Although issues like data quality and prediction reliability still need attention, artificial intelligence is already making drug discovery faster, more organized, and less dependent on trial-and-error. Overall, these technologies are helping researchers work more efficiently and move closer to developing safe and effective medicines in a shorter time.

Keywords: Artificial Intelligence, Drug discovery, Machine Learning, Protein Structure Prediction, Virtual Screening, ADMET Modeling.