£2 million has been awarded to 20 international teams harnessing AI to develop new treatments for ALS using “the largest ALS patient data set of its kind”. The Longitude Prize on ALS is awarding £100,000 each to 20 winners of its international “Discovery Award”, including teams spanning 70 organisations.
100 teams reportedly responded to a global call to action in June 2025, with awards made on their potential to use AI in the identification and validation of drug targets to support disease understanding and future drug discovery. The 20 selected will now gain access to a comprehensive ALS dataset comprising multiple types of biological information such as genomic sequences of 9,000 ALS patients.
The 20 winning teams include:
- ALS-AIM Consortium, training an AI to identify patterns from genes, molecules, and clinical information records of thousands of patients
- ALS/SPLICE, using AI to look at how splicing impacts the cell’s ability to process genetic information and how this goes wrong in patients with ALS
- ALS Genome-Ai Initiative, using techniques like the development of advanced machine learning architectures to study genomic resources and datasets
- ALS Matrix, looking to use AI to explore why some neurones survive in ALS while others don’t, mapping gene networks that protect resilient neurones, and identifying potential new drug targets
- ALS Precision Discovery Consortium – using AI to combine biological and clinical data from ALS patients to locate hidden disease subtypes and associated biological changes
- ALS Therapy Development Institute and Google Co-Scientist team will deploy Google’s Co-Scientist platform to develop therapeutic hypotheses using the datasets provided by the Longitude Prize
- Decode ALS will use AI models trained on whole genome sequencing data from ALS patients to test affected cells and proteins against possible drug treatments
- Espoir Biosciences will seek out therapeutic targets that slow and stimulate the repair of connections between neurones and muscles
- Latitude45 will use an AI-driven approach to analyse ALS patient datasets to uncover common molecular patterns
- MOSAIC ALS will use AI and patient data to map ALS subtypes and identify precision treatment targets
- OutSee Limited are developing an AI-powered engine to analyse human genetics and identify new drug targets
- Penn Medicine Against ALS plans to use AI to understand how the ALS protein TDP-43 disrupts gene regulation in the brain and to identify ten new drug targets
- Prima Mente is training an AI system to read the molecular fingerprints of thousands of ALS patients to find biological triggers for the disease
- Schmidt Center ALS Target Team will use medical and molecular data to investigate what cases neurones to go awry in different forms of ALS and look for potential drug targets
- ProjectMinE International Consortium team will be focusing on systematically resolving the molecular basis of sex differences in ALS
- Team La Jolla Labs-Unravel-Ulm will use tools for drug design and discovery along with data from ALS patients to identify novel targets with the hope of delivering faster and more personalised drug treatments
- Team Stormo is using an AI model to combine biological data from ALS patients into a knowledge graph to explore how genes interact as a system
- TUM ALIGN ALS will be using AI to look at patient’s biological data and identify hidden patterns to match patients with the right therapy earlier
- Vanderbilt NeuroCline is using AI to locate disease patterns in ALS datasets and better identify drug targets
- Vector ALS is unifying genetic, molecular, and clinical data in an AI system looking to decode ALS and pinpoint the genes that drive distinct disease forms, delivering up to ten proven targets to help treat the disease
UK organisations represented in these teams include King’s College London, Entelo, the University of Sheffield, the University of Oxford, Oxford PharmaGenesis, the University of Exeter, University College London, and the University of Edinburgh.
In 2027, ten of these teams will move on to the next phase, which will grant them a further £200,000 each to build an evidence base for their proposed therapeutic targets in silico and in the lab; and in 2028, five will receive £500,000 to pursue additional validation of the highest potential identified targets in the wet lab.
One winning team, to be announced in early 2031, will be given £1 million for identifying and validating the target offering the strongest evidence of therapeutic potential.
The prize is principally funded by the MND Association. Tanya Curry, the association’s chief executive, said: “Our vision is a world free from MND and this can be achieved through funding leading researchers to chase down new treatments. These 20 teams of innovators and their work can provide more understanding of this condition and potentially, one day, a cure. MND is a devastating disease, but every step forward in research brings hope. We are delighted to support the work that lies ahead in our role as principal funder.”
Wider trend: AI and health data
A US National Institutes of Health-supported study has developed an AI algorithm trained on EHR data to predict rare disease, with plans to scale over time to suggest when disease may appear, and how patients will respond to treatment. The WEakly Supervised Transformer (WEST) algorithm is reportedly capable of using “noisy”, incomplete, inaccurate, or non-informative data from EHRs to predict whether a patient is likely to have a specific rare condition. The algorithm was initially tested using EHR data from patients at risk for two rare lung diseases: pulmonary hypertension and severe asthma, achieving the highest rated predictive performance among all baseline models in identifying those diagnosed by clinicians.
UK government-backed Sovereign AI has announced the launch of its new £500 million fund, designed to give early-stage AI companies in the UK access to the funds, compute, and strategic assets needed to scale rapidly and compete on a global scale. The announcement offered an insight into what was on offer for UK AI providers, such as access to AI supercomputers with up to one million GPU hours per startup, early-stage investment of up to £20 million, and strategic assets to support with the creation of AI datasets and autonomous lab infrastructure. £282 million is also being dedicated to support AI startups with research and development, with a funding call to be announced for the creation of new datasets and other assets.
OpenAI has closed its latest funding round with $122 billion in committed capital, sharing plans to use the funding toward the next phase of at-scale AI, and in the development of an AI “superapp”. Referring to its success in generating $2 billion in revenue per month, the company states: “This is commercial scale, and it is mission scale. The fastest way to widen the benefits of AI is to put useful intelligence in people’s hands early and let that access compound globally. AI is driving productivity gains, accelerating scientific discovery, and expanding what people and organisations can build. This funding gives us the resources to continue to lead at the scale this moment demands.”
