ABSTRACT
Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize pharmaceutical biotechnology. AI has significant potential to augment target identification, lead compound optimization, and clinical trial design. This chapter aims to provide a comprehensive overview of the tools, techniques, and applications of AI in pharmaceutical biotechnology. The authors have outlined the applications of AI in drug discovery, development, supply chain management, and dosage formulation optimization. There have been discussions on how machine learning (ML) and deep-learning (DL) methodologies, including variational autoencoders (VAEs), generative adversarial networks (GANs), and neural language models (NLMs), are revolutionizing protein engineering and peptide design. The chapter includes real-world case studies to demonstrate the translational potential of AI in drug discovery projects and AI-assisted clinical trials. The authors also discussed the role of AI in developing personalized medicine. Despite significant advancements, AI implementation faces substantial challenges, including data scarcity, algorithmic transparency, and biological complexity representation. Moreover, future directions and emerging opportunities for AI with quantum computing and synthetic biology in pharmaceutical biotechnology have also been outlined. This chapter has implications for researchers, industry professionals, and policymakers to improve efficiency, accuracy, and decision-making in AI-assisted pharmaceutical biotechnology transformation.
