A Machine Learning Approach to Identify Small Molecule Inhibitors of Secondary Nucleation in α-Synuclein Aggregation

Abstract

AbstractDrug development is an increasingly active area of application of machine learning methods, due to the need to overcome the high attrition rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases where very few disease-modifying drugs have been approved. To address this problem, we describe a machine learning approach to identify specific inhibitors of the proliferation of α-synuclein aggregates through secondary nucleation, a process that has been implicated in Parkinson’s disease and related synucleinopathies. We use a combination of docking simulations followed by machine learning to first identify initial hit compounds and then explore the chemical space around these compounds. Our results demonstrate that this approach leads to the identification of novel chemical matter with an improved hit rate and potency over conventional similarity search approaches.