Artificial Intelligence has moved from a supportive role to the core of the discovery process. A 2025 Antibody Drug Discovery Market Trend report highlights that AI-enabled platforms are reducing average discovery timelines from several years to under 24 months. The AI/ML & Computational Design segment is expected to exhibit the highest CAGR through 2035 as pharmaceutical giants move away from traditional "trial-and-error" screening toward predictive in silico modeling.

In May 2025, several breakthroughs in Generative AI for Protein Design (such as AlphaFold3's expanded small-molecule and protein-ligand capabilities) have allowed researchers to identify structurally novel hits for over 70% of previously "undruggable" targets. This has led to a surge in partnerships, such as the Roche-Oxford BioTherapeutics collaboration, which utilizes the OGAP-Verify platform to discover novel targets for antibody-based therapeutics.

The economic impact of AI is profound: by filtering out low-probability candidates at the preclinical stage, AI tools are significantly lowering the 90% attrition rate currently seen in clinical trials. As computational costs continue to decline and "bio-specific" AI models become more accessible via public clouds, even mid-sized biotech firms are now able to compete with industry leaders in the design of complex multi-specific and T-cell engager (TCE) molecules.

FAQ: How is AI impacting antibody drug discovery? Ans: AI is reducing discovery timelines to under 24 months, improving the success rate of drug candidates, and allowing for the design of antibodies against previously "undruggable" targets.