Sygnature Discovery announced a strategic collaboration with DaltonTx to strengthen its AI-enabled drug discovery capabilities and accelerate medicinal chemistry decision-making while protecting customer data. The partnership integrates DaltonTx’s Dalton platform into Sygnature Discovery’s existing computational and medicinal chemistry toolkit, alongside proprietary and third‑party technologies such as SygDesign, BullFrog AI and Iktos, with the aim of enabling faster, more informed decisions across discovery programs.
DaltonTx’s platform combines drug discovery, software engineering and artificial intelligence, integrating data, models and experimental results into a single learning system that supports both biologics and small-molecule discovery. As part of the collaboration, Sygnature Discovery is running a retrospective evaluation using a legacy oncology program built around a small-molecule clinical candidate now in Phase I, to assess whether Dalton could have enabled more efficient candidate selection through better decision-making and reduced synthesis workload.
Sygnature Discovery CEO Simon Hirst said the company sees the future of AI in combining machine learning with the expertise of experienced scientists to reduce the number of compounds synthesized and tested, shorten design–make–test–analyze cycles, and speed progress toward candidate selection. Unlike many standalone AI tools, the Dalton platform pairs a secure, scalable backend with a natural language interface powered by agentic AI technologies, allowing scientists to use conversational workflows for ideation and problem-solving.
DaltonTx CEO and co‑founder Garry Pairaudeau said the next phase of AI in drug discovery is about measurable impact, and that organizations will gain the most value by connecting teams, tools and data into systems that enhance real-world discovery decisions. He said Dalton is designed to unify data, models and experimental results to capture what worked, what failed and why, so that judgment compounds over time, and highlighted the collaboration with Sygnature Discovery as focused on delivering tangible outcomes and measurable value.
The collaboration also addresses concerns about proprietary data use in AI environments. Sygnature Discovery said customer data and AI models are compartmentalized on a per‑program basis so that information from one project is not used to train models for others, and that its existing systems for secure data management extend to AI deployments. The company plans to expand validation of the Dalton platform into live customer programs as suitable opportunities arise.