Abstract
Natural marine products can help increase the quality of life in patients with neurological diseases. A large number of marine products act against Alzheimer's disease through varying pathways. According to structure- and ligand-based analyses, caulerpin, an alkaloid primarily isolated from the genus Caulerpa, possesses activity against monoamine oxidase B. To predict the activity of caulerpin, we employed Volsurf descriptors and the machine learning Random Forest algorithm in parallel with a structure-based methodology that included molecular docking. Using caulerpin as a lead compound, a database containing 108 analogs was evaluated, and nine were selected as active. The structures selected as active exhibited polar and non-polar substitutions on the caulerpin skeleton, which were relevant for their activity. Dragon consensus drug-like scoring was applied to identify the active analogs that might serve as good drug candidates, and the entire group presented satisfactory performance. These results indicate the possibility of using these analogs as potential leads against Alzheimer's disease.
Keywords
Alzheimer's disease; Caulerpin; Molecular docking; Monoamine oxidase B; Virtual screening
