Building the Next Generation of Biologics: Inside the Future of Protein Engineering

Biologics are rapidly expanding beyond traditional monoclonal antibodies into formats such as bispecific antibodies, antibody-drug conjugates, protein degraders, molecular glues, and AI-designed mini-proteins, many of which are now in clinical development 1.

Regulators are increasingly supporting novel biologic modalities, with dozens of bispecific antibodies approved globally and hundreds of ADCs and related complex constructs progressing through clinical pipelines 1.

Rising molecular complexity is shifting the key bottleneck in discovery from target identification to engineering and producing stable, manufacturable protein constructs at scale 1.

Specialized partners and CROs that integrate protein engineering, structural biology, and manufacturability assessment are seeing strong demand for help with bispecifics, ADCs, and multi-domain biologics 1.

High-throughput protein production and automated construct design now enable rapid testing of many sequence and format variants to identify versions best suited for structural studies and functional assays 1.

Artificial intelligence has moved beyond structure prediction to influence the entire protein-engineering cycle, using deep learning, transformer, and diffusion-based models to predict stability, solubility, yield, and manufacturability from sequence and structure data 1.

AI-driven advances have significantly improved accuracy in predicting protein properties such as stability and expression yields, accelerating early-stage engineering and reducing experimental trial-and-error 1.

Next-generation protein degraders such as PROTACs and molecular glues represent a major growth area, with well over one hundred degrader programs in clinical or preclinical stages worldwide 1.

AI-guided design combined with advanced structural biology techniques like cryo-EM is enabling structure determination and optimization of challenging systems, including flexible protein–protein interactions and ternary degrader complexes 1.

Companies are building large, high-quality proprietary datasets that include both positive and negative experimental results to train more realistic AI/ML models for protein behavior and manufacturability 1.

Future protein engineering is expected to rely on tightly integrated loops that connect AI-guided sequence design, structural insight, and experimental feedback to more reliably advance complex biologics 1.

Sources:

1. https://www.biospace.com/drug-development/building-the-next-generation-of-biologics-inside-the-future-of-protein-engineering