The use of artificial intelligence and big data in healthcare has advanced at remarkable speed over the past decade, rapidly reshaping how we diagnose, treat, and understand disease. Once experimental, AI is now becoming a trusted tool in clinical decision-making and medical innovation – playing an increasingly vital role in keeping people healthy.
In July, a robot trained on videos of previous surgeries was able to perform surgery independently (on an animal model) with 100% accuracy. There were 17 tasks to be completed during the surgery. The robot had to identify specific ducts and arteries; grab hold of them; strategically place clips; and sever parts with scissors. It did so with total precision – an extraordinary achievement.
AI is being used to read chest X-rays to identify cancers far before the human eye notices them, helping oncologists catch and treat lung cancer earlier than ever. AI has been used to help a couple conceive a baby after 18 years of trying – the method uses AI to identify and recover hidden sperm in samples from men who were thought to be infertile.
The role of AI in drug discovery
While AI is transforming healthcare in hospitals, its potential expands to the entire health landscape, not least the discovery of new drugs.
This is a critical step for diseases where meaningful treatment options are currently limited, as is the case with many neurodegenerative diseases where cells of the central nervous system stop working or die, like Alzheimer’s Disease, Parkinson’s Disease, and amyotrophic lateral sclerosis (ALS) – the most common form of motor neurone disease (MND).
ALS is a progressive neurodegenerative disease that damages the motor neurones in the brain and spinal cord. Signals from the brain stop reaching muscles, leading to severe muscle degeneration. Eventually this affects the muscles that are used to swallow food and drink, and those used to breathe.
There are approximately 140,000 new cases of ALS diagnosed worldwide each year – 384 new cases every day. It’s predicted that ALS cases will increase from around 223,000 in 2014 to 377,000 in 2040.
Neurodegenerative diseases are extraordinarily complex. In conditions like ALS, the question of why motor neurones die in some people but not others remains one of the most urgent and difficult to answer. This complexity has long hindered the development of effective treatments, leaving patients with few therapeutic options. ALS, in particular, has no long-term treatments, and no cure.
ALS is a heterogeneous disease – meaning it has multiple genetic and environmental causes. Moreover, it involves a cascade of biological disruptions: from faulty RNA processing and protein misfolding to inflammation and cellular transport failure. These overlapping malfunctions make it nearly impossible to isolate a single cause or intervention point.
AI thrives on complexity. By analysing vast datasets across genetics, cell biology, and clinical records, AI can detect patterns and predict disease mechanisms in ways that were previously out of reach.
Crucially, it can also model how potential drugs might interact with biological targets, accelerating what has traditionally been a slow, trial-and-error process. For a disease as heterogeneous and under-treated as ALS, this speed and precision offer a much-needed path forward.
Hope is growing, not just among scientists, but also among patients and families who have waited too long for progress.
Unlocking that progress, however, depends not only on advanced tools like AI, but also on access to the right data. Fortunately, ALS does have data – and lots of it – generated in the last decade thanks to global fundraising efforts like the Ice Bucket Challenge.
Historically, ALS research data has been fragmented across various institutions, each with their own access requirements and not curated to be interoperable. The Longitude Prize on ALS is changing that.
This £7.5 million challenge prize is designed to incentivise and reward the use of AI-based approaches to transform drug discovery for the treatment of ALS. Principally funded by the MND Association, and designed and delivered by Challenge Works, supported by Nesta, alongside global funders, the prize is turning the tide on a disease that has long resisted conventional research strategies.
Delivering on a decade of ALS data
The prize has convened one of the largest and most comprehensive collections of ALS patient data for researchers and AI innovators to use. It has been curated to be interoperable, so that AI algorithms can be trained to scour through the biology of the disease and identify the most promising drug targets for future treatments.
The dataset combines multiple types of high-quality biological data that have not previously been available in a single place, made available to innovators via DNANexus, hosted on Amazon Web Services.
It includes whole genome sequencing (WGS) from more than 9,000 ALS cases and over 3,500 controls, sourced from leading ALS research initiatives including Project MinE, ALS Compute, New York Genome Center (NYGC), ALS Therapy Development Institute and Answer ALS.
These datasets are harmonised and will be securely shared via a cloud platform, ensuring seamless access to high-quality genome information.
Participants will also have access to comprehensive multi-omics data, which includes epigenomics, transcriptomics, and proteomics data from over 2,000 cases. This comprehensive molecular information enables exploration of novel disease mechanisms and opens the door to innovative drug target discovery.
Critically, the biological data is accompanied by relevant clinical information, allowing researchers to connect genetic and molecular findings with real-world patient characteristics. This integrated approach offers unprecedented opportunities to uncover meaningful correlations between genotype, molecular function, and phenotype.
Identifying drug targets is the essential first step toward developing new treatments. With AI now able to accelerate this process at scale, researchers and innovators have a unique opportunity to change the trajectory of ALS, and the lives of thousands of people affected by it.
Bringing together bright minds for winning solutions
However, it’s safe to say that not all of those working in the field of AI and big data will be experienced working on neurodegenerative diseases.
The Longitude Prize on ALS is addressing this by helping innovators with strong ideas and credible backgrounds (whether individuals or organisations) to team up with others that offer complementary expertise via a ‘match making’ process. For example, a computational biologist could be paired with an ALS researcher.
Teams will be judged on the potential for their approach to identify and validate drug targets driving understanding of the disease and supporting onward translation into drug discovery, with the entry window open until 3 December 2025.
In April 2026, 20 teams will be awarded £100,000 ‘Discovery Awards’ to identify new high potential therapeutic targets.
In May 2027, 10 of these will receive a further £200,000 to build the evidence base for their proposed therapeutic targets in-silico, using computational models to test their research.
In September 2028, five teams will receive £500,000 to undertake validation of the highest potential identified targets in the wet lab to further test their research.
The winning team will be announced in early 2031 and will be awarded £1 million for identifying the target with the strongest evidence of therapeutic potential.
The application of AI, in conjunction with one of the biggest global databases of patient data, will allow researchers and innovators to unlock the complexity of ALS. If the last decade has been focused on generating robust data about the disease, AI now enables us to turn our attention to making extraordinary, rapid strides towards lasting treatments.
The Longitude Prize on ALS serves as further evidence of AI’s ever-expanding utility and marks the start of transformative change for ALS drug discovery.
