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For an antibody candidate, strong target binding is only part of the path toward a successful therapeutic. A molecule can show high affinity and selectivity yet encounter problems later because of poor stability, low expression, aggregation, non-specific binding, or other unfavorable biophysical properties.

Explore VHH-based biosensors for high-sensitivity detection using nanobodies in electrochemical, SPR, and optical diagnostic platforms.

Biointron’s Antibodies After Hours: BIO 2026 Reception was held at The Lion's Share in San Diego on Tuesday, June 23, 2026. Held on the second day of the BIO International Convention, we had a memorable evening of networking, drinks, and delicious appetizers with members of the biotech and pharma community at one of San Diego’s most distinctive downtown venues!

Explore how VHH antibodies stabilize protein conformations for crystallography, cryo-EM, and structural biology applications in protein structure studies.

Explore VHH antibody production strategies for molecular imaging probes, including PET, SPECT, MRI, and optical imaging applications using nanobodies.

Artificial intelligence and machine learning are expanding the number of antibody sequences that can be designed, ranked, and optimized computationally. However, a predicted antibody sequence still must be expressed and tested before researchers can determine whether it produces a functional protein with suitable binding and developability characteristics.

June 2026 included acquisitions of late-stage antibody developers, partnerships involving multispecific and AI-enabled discovery platforms, and financings supporting programs across oncology, immunology, ophthalmology, neurology, and infectious disease.

AI design still depends on experimental validationAI-driven antibody design is a process involving data curation, model development, candidate generation, computational filtering, and experimental validation.

Two popular topics discussed at the 2026 BIO International Convention were developments in antibody-drug conjugates (ADCs) and AI in biology. Held on June 22-25 in San Diego, California, read on for a quick recap.

AI is changing antibody discovery, but model performance depends on the quality of the experimental data used to train, validate, and refine predictions. Antibody researchers increasingly need more structured, comparable, and biologically meaningful datasets that link sequence design to expression, binding, and developability outcomes.

Antibody discovery has become increasingly sequence-rich. Display technologies, single B cell workflows, computational design, and AI-enabled antibody engineering can now generate large numbers of candidate sequences for evaluation. However, a larger sequence pool does not automatically translate into better lead candidates.

Biointron, a leading contract research organization specializing in antibody discovery, expression, and optimization for global biotechnology and pharmaceutical companies, has released RushData, a new service for antibody discovery uniquely suited for AI/ML driven workflows.AI-driven antibody