Identification of high-affinity secondary nucleation inhibitors of Aβ42 aggregation from an ultra-large chemical library using Deep Docking

Abstract Alzheimer’s disease is characterized by the aggregation of the Aβ peptide into amyloid fibrils. According to the amyloid hypothesis, targeting pharmacologically Aβ aggregation could result in disease-modifying treatments. The identification of inhibitors of Aβ aggregation, however, is complicated by complex technical challenges, which typically restrict to tens of thousands the number of compounds that can be screened in experimental aggregation assays. Here, we report a computational route to increase by 4 orders of magnitude the number of screenable compounds. We achieve this result by developing an open source pipeline version of the Deep Docking protocol, and illustrate its application to the discovery of secondary nucleation inhibitors of Aβ aggregation from an ultra-large chemical library of over 539 million compounds. The pipeline was used to prioritize 35 candidate compounds for in vitro testing in Aβ aggregation assays. We found that 19 of these compounds inhibit Aβ aggregation (54% hit rate). The two most potent compounds showed potency better than adapalene, a previously reported potent inhibitor of Aβ aggregation. Consistent with the intended mechanism of action, these two compounds also proved to be high-affinity binders of Aβ fibrils with an equilibrium dissociation constant in the low nanomolar range in surface plasmon resonance experiments. These results provide evidence that structure-based docking methods based on deep learning represent a cost-effective and rapid strategy to identify potent hits for drug development targeting protein misfolding diseases.