AI FOR DRUG DISCOVERY: TRANSFORMATIVE APPROACHES

Abstract

The integration of artificial intelligence (AI) into drug discovery has revolutionized the pharmaceutical industry, significantly accelerating target identification, molecular design, and clinical trial optimization. This paper explores the latest advancements in AI-driven drug discovery, focusing on deep learning, generative models, and reinforcement learning for de novo drug design, virtual screening, and toxicity prediction. We examine case studies of AI-discovered drugs in clinical trials, such as Insilico Medicine’s INS018_055 (a first-in-class AI-generated drug for fibrosis) and Exscientia’s DSP-1181 (an AI-designed OCD treatment). Additionally, we discuss challenges in data scarcity, model interpretability, and regulatory compliance, while outlining future trends, including quantum machine learning for molecular simulations and federated learning for multi-institutional collaboration.