Abstract
Being the leading cause of death and disability in China, stroke encompasses a number of risk factors, one of them being genetic mutations. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most prevalent genetically induced stroke. This study investigates the feasibility of utilizing iterative screening rounds to identify potential drug candidates that effectively target the Notch3 R90C mutant protein associated with CADASIL. Through multiple rounds of molecular docking using AutoDock Vina and structural predictions by AlphaFold, we systematically narrowed down a large set of small molecules from the DrugBank database to identify those with the highest binding affinities. The research highlights the effectiveness of leveraging structural data and hierarchical clustering in refining the selection process, ultimately enhancing the precision in identifying promising therapeutic agents.
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