Vol. 7, Issue 2, Part E (2025)

It includes how Artificial intelligence (AI) and machine learning (ML) are transforming the pharmaceutical sector, especially in drug discovery and development. It provides a comprehensive review of recent advancements from 2019 to 2024 regarding AI-driven approaches, such as deep learning, generative adversarial networks, and data-driven methodologies, that accelerate and improve various stages of drug research. The document evaluates innovative model architectures, cloud-based implementations, and their roles in preclinical analysis, safety, hit/lead discovery, and target identification. It highlights the challenges in traditional drug discovery, including long timelines, high costs, and low success rates, and demonstrates how AI and ML address these by enhancing virtual screening, lead optimization, and target validation. Ultimately, the work underscores that robust AI integration can revolutionize pharmaceutical R&D, offering promising solutions to accelerate innovation and optimize patient outcomes.