Biointron hosted Antibodies After Hours: Antibodies Meet AI on October 2, 2026, at Catalyst Restaurant in Cambridge, MA. The evening brought together scientists and industry professionals working in antibody discovery and AI-driven drug discovery for an engaging reception featuring scientific discussions, networking, dinner, and a raffle!
We thoroughly enjoyed reconnecting with familiar faces and welcoming new members of the local antibody research community!
The evening began with an open bar and appetizers, giving attendees an opportunity to connect with peers and exchange ideas. Gang Liu, Director of Discovery Services at Biointron, welcomed guests and introduced Biointron's capabilities in antibody discovery, production, and experimental characterization.
A central theme of the introduction was the growing importance of high-throughput experimental validation in AI-driven antibody discovery. As computational approaches enable researchers to generate thousands of antibody candidates in increasingly shorter timeframes, experimental production and characterization can become limiting steps in the discovery process.
Dr Liu introduced RushDATA™, Biointron's integrated wet-lab data platform designed to support rapid antibody production and standardized experimental data generation at scale. By connecting antibody sequences with measurable properties such as expression, binding, affinity, and developability, RushDATA™ helps accelerate the design–build–test–learn cycle and provides structured data to support AI model development.
Our guest speaker, Dr. André A. R. Teixeira, Senior Director of the Antibody Platform at the Institute for Protein Innovation (IPI), delivered an insightful presentation on scaling antibody discovery and applying machine learning to predict antibody developability directly from sequence information.
Dr. Teixeira began by highlighting the significant opportunities that remain in antibody discovery. Although more than 5,000 extracellular and cell-surface human proteins are potentially addressable by antibodies, only approximately 5% have been explored through therapeutic antibody programs. Many biologically important proteins remain underexplored, emphasizing the need for scalable discovery platforms capable of generating and characterizing antibodies against a broader range of targets.
A major focus of the presentation was DELPHI, an interpretable, open platform for sequence-based antibody developability prediction. DELPHI applies machine learning approaches to predict properties such as polyreactivity and aggregation risk, helping researchers identify potential developability liabilities earlier in antibody discovery.
Dr. Teixeira shared findings showing that protein language model representations can capture antibody sequence characteristics, but do not automatically distinguish between favorable and unfavorable developability outcomes. His research also demonstrated how model interpretability can identify sequence features associated with polyreactivity, particularly within the complementarity-determining region H3 (CDR H3).
These findings highlight the potential of combining large-scale experimental datasets with interpretable machine learning to improve antibody candidate selection and guide sequence optimization. The presentation reinforced the importance of connecting experimental evidence with computational prediction to enable more informed decisions throughout antibody discovery.
Following the scientific presentations, attendees continued their conversations over a dinner buffet, exchanging perspectives on antibody discovery, AI applications, and emerging challenges in therapeutic development.
The evening concluded with our raffle, where lucky winners received AirPods Pro, Amazon Echo Dots, and YETI tumblers!
Thank you to everyone who joined us at Catalyst Restaurant for Antibodies After Hours! We sincerely appreciate the enthusiasm, thoughtful conversations, and participation throughout the evening.
A special thank-you to Dr. André Teixeira for sharing his research and insights into high-throughput antibody discovery and AI-driven developability prediction.
Biointron looks forward to hosting more opportunities to bring together scientists working across antibody discovery, protein engineering, and AI-driven drug development.
We hope to see you at our next event!
Interested in learning more about Biointron's antibody discovery and experimental validation services? Our team would be happy to discuss how we can support your research.
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