DaltonTx combines expertise in drug discovery, software engineering and artificial intelligence through its Dalton platform, which integrates data, models and experimental results into a single learning system to support biologics and small molecule discovery programmes.
The collaboration brings DaltonTx into Sygnature Discovery’s existing computational and medicinal chemistry toolkit alongside proprietary and third-party technologies, including SygDesign, BullFrog AI and Iktos.
By combining multiple complementary AI platforms, Sygnature Discovery aims to support faster, more informed decision-making across discovery programmes.
As part of the collaboration, Sygnature Discovery is conducting a retrospective evaluation using a legacy oncology programme focused on a small molecule clinical candidate now in Phase I development.
The analysis is assessing whether the platform could have enabled the team to reach candidate selection more efficiently through improved decision-making and reduced synthesis burden.
“AI in drug discovery continues to evolve rapidly, but we believe the future lies in combining the power of machine learning with the expertise and intuition of experienced scientists,” said Simon Hirst, CEO of Sygnature Discovery.
“By helping scientists make better-informed decisions earlier in the discovery process, we can reduce the number of compounds synthesised and tested, shorten DMTA cycles and accelerate progression toward candidate selection.”
Unlike many standalone AI applications, Dalton combines a secure and scalable backend architecture with a natural language interface powered by agentic AI technologies, enabling scientists to drive ideation and problem-solving through conversational workflows.
“The next phase of AI in drug discovery is about impact,” said Garry Pairaudeau, CEO and cofounder of DaltonTx.
“We believe the organisations that capture the most value from AI will be those that connect their teams, tools and data into systems that improve real-world discovery decisions."
"Dalton unifies data, models, and experimental results to capture what worked, what failed and why, with full context so judgment compounds with time. We are proud to collaborate with Sygnature Discovery to deliver tangible outcomes that translate into measurable value.”
The collaboration also addresses growing industry concerns surrounding proprietary data use in AI-enabled environments.
Sygnature Discovery confirmed that customer data and AI models are compartmentalised on a per-programme basis to ensure customer information is not used to train models supporting other projects.
“Our approach ensures customers can access the latest AI-enabled technologies without their data being used to train models for the benefit of others,” added Hirst.
“We have mature systems in place for securely managing customer data, and those same principles apply when deploying AI tools.”