Book Title: Multidisciplinary Research in the Age of Artificial Intelligence
Chief Editors: Dr. Jagdish Kumar Sahu, and Dr. Byram Anand
Associate Editors: Dr. J. Jayamani, and Albeen Josebh Ahmed
Co-Editors: Dr. Sunakshi Verma, and Mr. Pankaj Bharat Devre
ISBN: 978-93-7183-018-8
Chapter: 27
DOI: https://doi.org/10.59646/781/27
Author: Syeda Nishat Fathima
Abstract
Generative Artificial Intelligence (AI) is transforming the landscape of drug discovery by enabling the design of novel chemical entities with improved efficiency, accuracy, and reduced development time. Unlike conventional drug discovery approaches that rely on screening existing molecular libraries, generative AI models such as variational autoencoders (VAEs), generative adversarial networks (GANs), transformer-based architectures, graph neural networks, and diffusion models can generate entirely new molecular structures with desired pharmacological and physicochemical properties. These models integrate predictive algorithms for target affinity, absorption, distribution, metabolism, excretion, and toxicity (ADMET), thereby facilitating multi-objective optimization during the earliest stages of drug design. This chapter discusses the conceptual foundations of generative AI, molecular representation techniques, major generative architectures, and their applications in de novo molecular generation, lead optimization, structure-based drug design, drug repurposing, polypharmacology, and protein structure prediction. It also highlights recent translational successes and emerging industrial applications demonstrating the potential of AI-generated molecules in clinical development. Despite challenges related to data quality, interpretability, synthetic feasibility, regulatory acceptance, and ethical considerations, generative AI represents a paradigm shift in pharmaceutical research. The integration of advanced computational intelligence with medicinal chemistry and pharmacology is expected to accelerate the development of safer, more effective, and personalized therapeutics.
Keywords: Generative Artificial Intelligence, Drug Discovery, De Novo Drug Design, Deep Learning, Variational Autoencoders, Generative Adversarial Networks, Transformer Models, Molecular Generation, Pharmacology, ADMET Prediction.