DaltonTx launches AI drug discovery platform 

DaltonTx has launched an AI drug discovery platform which could help scientists “make better-informed decisions earlier in the discovery process”. 

The platform, called Dalton, seeks to integrate agentic AI, human expertise and experimental data into a continuous learning engine.  

DaltonTx says the platform links molecules, assays, models, experimental outcomes, project context and human decisions into a single learning system, not just to capture what happened but also why a particular path was chosen. 

The company says this could help scientific teams discover new small molecules and biologics by supporting every step of the design-make-test-analyse cycle.  

Alongside the drug discovery platform launch, DaltonTx has announced a collaboration with Sygnature Discovery. The agreement will integrate Dalton with Sygnature Discovery’s existing computational and medicinal chemistry toolkit alongside proprietary and third-party technologies including SygDesign, BullFrog AI and Iktos.

“The next phase of AI in drug discovery is about impact,” said Dr Garry Pairaudeau, CEO and Co-Founder of DaltonTx.  

“We believe that the organisations capturing the most value from AI will be those that connect their teams, tools and data into systems that improve real-world discovery decisions.  

“Our Dalton drug discovery platform unifies data, models, and experimental results to capture what worked, what failed and why, with full context, so judgement compounds over time.  

“We are proud to be working with such a respected industry partner as Sygnature Discovery, and it is a great endorsement of the tangible outcomes that Dalton can translate into measurable value.” 

“We are excited to be working with DaltonTx and see how they can help scientists make better-informed decisions earlier in the discovery process,” added Simon Hirst, CEO of Sygnature Discovery, “so that we can reduce the number of compounds synthesised and tested, shorten DMTA cycles and accelerate progression toward candidate selection.” 

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