Drug Programme

Amyotrophic Lateral Sclerosis.


What is ALS?

ALS, also known as motor neurone disease or Lou Gehrig's disease, is a devastating condition which causes the death of neurons controlling voluntary muscles, leading to difficulty speaking, swallowing, and eventually breathing. There is no cure, and current treatments only average a three month extension of life.  ALS is a very complex disease involving both motor neurons and  the surrounding non-neuronal cells. Over 30 genes have been implicated, yet ~85% of patients do not have mutations in these genes. In these patients, the scientific community still does not understand what the causes are.

New target validation

BenevolentAI's platform produced a ranked list of potential ALS treatments, together with biological evidence. Our team was able to rapidly triage these predictions using strategies focused on pathways implicated in multiple ALS processes. The five most promising compounds were taken to the Sheffield Institute for Translational Neuroscience (SITraN), a world authority on ALS. An ALS lead molecule emerged from a breast cancer drug, which showed delay of symptom onset when tested in the gold standard disease model.

Fast lead optimisation

Our platform also supports the ALS programme through lead optimisation to identify a clinical candidate. EvoChem is a tool that generates de novo compound ideas in order to reduce the number of compounds synthesised and consequently the number of experimental cycles required per programme. This AI-augmented molecular design platform is helping to design active compounds utilising multiple predictive algorithms. We now generating results with more diverse polypharmacology.

smart patient stratification

Benevolent's platform uses a large, multi-trial ALS database to contextualise and reason on this data. This enabled us to determine progression endotypes to inform our pre-clinical and future clinical development. We are currently working with academics and charities to explore collaborations with our Translational Medicine Squad, dedicated to building machine learning models for reasoning upon deep patient-level and ‘omics datasets.


Our most recent workflows for hypothesis generation have resulted a lead molecule, BEN-XX1, an optimised compound showing improved CNS exposure and profound rescue effect in ALS patient cells.  We are currently in late stage lead optimisation of BEN-XX1 aiming to nominate a clinical candidate as soon as possible.


"This is the most exciting result I have seen in my life"

Dr Matthew Stopford, SITraN

"AI will lead to a massive acceleration in hypothesis generation and new findings… leading to faster drug discovery."

Dr Laura Ferraiuolo of SITraN, one of the world leading centres for research into ALS


Using machine learning to improve target prediction