Book Title: Advanced Studies in Multidisciplinary Research and Innovation (ASMRI)
Chief Editors: Dr. Jagdish Kumar Sahu and Dr. Krishna Ashutoshbhai Vyas
Associate Editors: Dr. N. Ramesh Chandra Srikanth and Dr. Lourdu Vesna J
Co-Editors: Dr. Aarti Sharma and Dr. Pushpa Mamoria
ISBN: 978-93-7183-010-2
Chapter: 4
DOI: https://doi.org/10.59646/745/4
Authors: Dhiraj Baishya and Priyam Jyoti Das
Abstract
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology with profound implications for pharmaceutical and biomedical research. Powered by advanced machine learning algorithms and large language models (LLMs), GenAI systems can generate human-like text, predict molecular structures, assist in drug discovery, and support various stages of scientific communication. The integration of GenAI into pharmaceutical research has accelerated target identification, virtual screening, lead optimization, and prediction of pharmacokinetic and toxicological properties, thereby reducing the time and cost associated with conventional drug development pipelines. In biomedical sciences, GenAI has enhanced data interpretation, personalized medicine, and clinical decision-support systems. Furthermore, the rapid adoption of tools such as ChatGPT, Gemini, Claude, and Copilot has transformed scientific writing by facilitating literature reviews, manuscript preparation, language refinement, and grant proposal development. Despite these advantages, significant concerns remain regarding reliability, hallucinated outputs, algorithmic bias, data privacy, copyright issues, and research integrity. Academic publishers and scientific organizations have therefore introduced guidelines governing the responsible use of AI-assisted technologies in research and scholarly communication. As generative models continue to evolve, their role is expected to expand beyond automation toward collaborative intelligence, where human expertise and artificial intelligence work synergistically to accelerate scientific innovation. This chapter explores the fundamental principles of Generative AI and critically examines its emerging applications in drug discovery, biomedical research, scientific writing, and academic publishing. Additionally, the chapter discusses ethical considerations, current limitations, and future prospects of GenAI in advancing pharmaceutical and biomedical sciences.
Keywords: Generative Artificial Intelligence; Large Language Models; Drug Discovery; Biomedical Research; Scientific Writing; Academic Publishing