Genai for Drug Discovery and Development

Abstract

The integration of Generative Artificial Intelligence (GenAI) in drug discovery and development has revolutionized pharmaceutical research by accelerating the identification of novel therapeutics while reducing costs and failure rates. Traditional drug development is time-intensive and capital-heavy, but GenAI offers probabilistic solutions for therapeutic activities, molecular formation, and enhanced drug formulation predictions. By leveraging deep learning, natural language processing, and generative models, GenAI optimizes multiple phases of drug discovery, from target identification to clinical trial enhancements. This study explores the transformative role of GenAI in predictive modeling, biomarker identification, and drug redesigning, illustrating its potential through case studies and computational models like Variational Autoencoders, Generative Adversarial Networks, and Reinforcement Learning Models. Additionally, the paper discusses regulatory challenges and ethical concerns surrounding AI-driven drug discovery. Findings suggest that GenAI significantly enhances efficiency in virtual screening, molecular docking, and pharmacokinetics prediction, demonstrating its potential to reshape pharmaceutical innovation