Discovery of potent inhibitors of -synuclein aggregation using structure-based iterative learning

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of alpha-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of alpha-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones. Developing disease-modifying drugs for neurodegenerative diseases has been very challenging. Now a machine learning approach has been used to identify small molecule inhibitors of alpha-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Compounds that bind to the catalytic sites on the surface of the aggregates were identified and then progressively optimized into secondary nucleation inhibitors.