Related Experiment Video
Updated: Jul 14, 2026
ALS - Motor Neuron Disease: Mechanism and Development of New Therapies
Published on: July 29, 2007
Repurposing FDA-approved drugs for treatment of amyotrophic lateral sclerosis using machine learning
Saanvi Dogra1, Valentina L Kouznetsova2,3,4, Igor F Tsigelny2,3,4,5
1MAP Program, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA.
Introduction:
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by loss of motor neurons. Current medications are largely ineffective, associated with side effects, and hindered by a lack of agreement over treatment pathways. The time-intensive process and high costs further limit the development of therapeutics. Therefore, this research aimed to identify FDA-approved drugs that inhibit three proteins (Casein kinase 1, Protein tyrosine kinase 2, Ephrin type-A receptor 4) associated with ALS.
Methods:
A machine learning (ML) model was trained for each protein to identify an inputted compound as an active inhibitor of that protein. The FDA-approved drugs were then screened through these models, and 18 drugs were identified as likely inhibitors for all three proteins. The results were validated through protein-ligand docking of each drug to its respective protein(s).
Results:
Risperidone was the most active drug, with an average ML score of 1 and binding affinity of -8.9. The ML scores and binding affinities had a strong correlation, indicating reliability.
Conclusion:
This research predicted multiple drugs that can simultaneously target many proteins involved in ALS, creating more effective treatment options at a lower cost. This procedure can be applied to efficiently discover drugs for other diseases in the future.
Insights
Researchers identified FDA-approved drugs to inhibit key proteins in Amyotrophic Lateral Sclerosis (ALS). This approach offers a faster, cheaper path to new ALS treatments by repurposing existing medications.
Area of Science:
- Neuroscience
- Pharmacology
- Computational Biology
Background:
- Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease impacting motor neurons.
- Current ALS treatments are limited by ineffectiveness, side effects, and high development costs.
- Targeting multiple proteins simultaneously presents a promising therapeutic strategy.
Purpose of the Study:
- To identify existing FDA-approved drugs capable of inhibiting three specific proteins implicated in ALS: Casein kinase 1, Protein tyrosine kinase 2, and Ephrin type-A receptor 4.
- To leverage machine learning and computational methods for accelerated drug discovery in ALS.
Main Methods:
- Development of machine learning models to predict inhibitors for each target protein.
- Screening of FDA-approved drug libraries using the trained machine learning models.
- Validation of predicted drug candidates through protein-ligand docking simulations.
Main Results:
- 18 FDA-approved drugs were identified as potential inhibitors for all three target proteins.
- Risperidone emerged as the most promising candidate, exhibiting high machine learning scores and strong binding affinity.
- A significant correlation was observed between machine learning predictions and binding affinities, confirming model reliability.
Conclusions:
- This study successfully identified potential multi-target inhibitors for ALS from existing FDA-approved drugs.
- The findings suggest a cost-effective and efficient approach for developing novel ALS therapeutics.
- The methodology can be extended to accelerate drug discovery for other complex diseases.
Related Concept Videos
01:26Heart Failure Drugs: Inhibitors of Renin-Angiotensin System
01:29Drugs for Treatment of Crohn's Disease in IBD Using Immunomodulatory Agents
01:21Drugs for Treatment of Crohn's Disease in IBD Using Glucocorticoids
01:26FDA Approved Drugs: Changes to Approved Drugs
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions