• New capabilities enable researchers to analyse, characterise and prioritise antibody candidates at scale 
• Integrated AI models and workflows support better informed decisions about which antibodies to progress experimentally

Dalton^Tx, the decision engine for drug discovery, announced the launch of advanced antibody discovery capabilities within the Dalton platform.

The platform brings together antibody design, structure prediction and validation tools within a single workflow. By continuously testing AI-generated outputs against real experimental data, it helps researchers identify candidates that are not only scientifically plausible, but more likely to succeed in development.

Dalton is operated through a chat interface which captures the rationale behind each decision: why candidates were discarded, how priorities were set and which goals drove each stage of the campaign. A purpose-built ontology links scientists' observations and decisions to the specific antibodies designed and selected. Overall, this means project knowledge accumulates rather than being lost between meetings, handovers and team changes.

Dr Garry Pairaudeau, CEO and Co-Founder of Dalton^Tx, commented: “Researchers today have access to an unprecedented range of AI models and computational tools for antibody discovery, but turning those outputs into confident scientific decisions remains a challenge. We built these capabilities to bring data, models and expert judgement together in one coherent workflow. Ultimately, we're helping teams reduce complexity, accelerate discovery and focus resources on the most promising opportunities.”

The Dalton platform can evaluate antibody repertoires comprising millions of sequences in a matter of hours. Recently, Dalton used the platform to fold the entire 2.6 million paired OAS space at a rate of 87,000 structures per hour. Researchers can use the platform for hit identification and optimisation all the way to candidate discovery and design of complex antibody formats, such as bispecifics. The outputs are designed to inform scientific review and experimental planning, helping teams focus laboratory resources on the candidates with the strongest rationale for progression.

Professor Charlotte Deane, Co-Founder and Chief AI Officer at Dalton^Tx, added: "Antibodies are the most successful class of medicines we have, yet we still discover them largely by trial and error. There has never been a platform before that puts the necessary tools in the hands of every biotech, CRO and pharmaceutical company in the world. I co-founded Dalton^Tx to deliver the impact I knew this science could have, and I'm delighted to see that platform released today."

The launch is the latest step in Dalton^Tx’s strategy to build intelligent drug discovery workflows that connect advanced AI, experimental data and human expertise. The company continues to collaborate with the University of Oxford and other academic researchers to evaluate emerging approaches in antibody modelling and analysis.