AI-Driven Design Platforms of Next-Generation Antibody Therapeutics

Artificial intelligence (AI) is reshaping drug discovery by bridging the gap between traditional computer-aided drug design (CADD) and next-generation, data-driven methodologies. Unlike conventional CADD, which relies on physical modelling of molecular interactions, AI integrates machine learning (ML) and deep learning (DL) to leverage rapidly expanding datasets in biology and chemistry. These approaches enable efficient prediction of molecular structures, binding affinities, and pharmacological properties, thereby reducing both time and cost in drug development. The impact is particularly profound in the design of protein therapeutics, such as antibodies, which require accurate modelling of complex structures and interactions. Emerging AI frameworks, including generative adversarial networks (GANs), reinforcement learning (RL), and multi-omics integration, are accelerating target identification, optimizing lead candidates, and refining pharmacokinetic and biophysical profiles. In this review, we highlight recent advances at the interface of AI and antibody drug discovery, discuss key methodological developments, and examine the challenges that remain in translating AI-driven strategies into clinical success. We further explore how AI-enabled platforms are redefining the landscape of precision biopharmaceuticals, offering new opportunities for efficient and targeted therapeutic development.