Abstract: Journal of Medical Physics and Biophysics Simulations

Abstract:
The convergence of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), with biotechnology represents a fundamental paradigm shift in the life sciences. Traditional discovery meth ods, characterized by high costs, long timelines, and high failure rates, are insufficient to manage the expo nential growth of multi-modal biological data (genomics, proteomics, clinical records). This paper reviews the transformative impact of AI across four critical sectors: Drug Discovery and Development (D3), Genomics and Precision Medicine, Protein Engineering, and Synthetic Biology. We analyze the state-of-the-art AI archi tectures driving these innovations, including generative models and foundation models. Finally, we discuss the critical challenges data scarcity, model interpretability, and ethical governance that must be addressed to fully realize the promise of the algorithmic engine of life.