Authors: Kriti.R. Shukla
Abstract: As of 2026, the biotechnology sector has undergone a fundamental paradigm shift from a traditional "wet-lab first" experimental model to an "in silico first" computational framework. This evolution is driven by the maturation of generative artificial intelligence (AI), geometric deep learning, and multi-modal foundational models. This article explores the current state of AI in biotechnology, focusing on protein engineering, generative chemistry, genomic interpretation, and bioprocess optimization. We examine how the integration of Large Language Models (LLMs) and diffusion-based generative models has accelerated the drug discovery pipeline, reduced R&D costs, and enabled the design of de novo biological systems with unprecedented precision.
