Structure-based Generation of a Secondary Nucleation Inhibitor in alpha-Synuclein Aggregation Using a Conditional Diffusion Model

The process of α-synuclein aggregation results in the formation of amyloid fibrils, which accumulate in the brain of patients affected by Parkinson's disease. Among possible therapeutic strategies to cure this disease, one approach is based on the development of compounds capable of inhibiting alpha-synuclein aggregation. An effective inhibition could be achieved by blocking the nucleation sites on the surface of the amyloid fibrils that are responsible for their autocatalytic proliferation. Here, we report a strategy based on deep learning to achieve this goal, which uses an E(3)-equivariant conditional diffusion model. By using this approach, we designed and tested experimentally candidate small molecules. We found that one of these small molecules acts as a potent inhibitor of secondary nucleation in alpha-synuclein aggregation. These results provide evidence that generative diffusion models offer effective tools for drug design.