Exploration and Exploitation Approaches Based on Generative Machine Learning to Identify Potent Small Molecule Inhibitors of α-Synuclein Secondary Nucleation

The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting alpha-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of alpha- synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.

Automated summary

The lack of approved disease-modifying drugs for Parkinson's disease is a major concern. Previous clinical trials targeting alpha-synuclein aggregation have faced challenges in identifying effective compounds. In this study, a machine learning approach was used to find small molecules that can reduce the production of oligomeric species through perturbing the kinetics of aggregation.