AlzyFinder represents a pioneering platform at the forefront of bioinformatics research, integrating state-of-the-art machine learning models to predict the probability of ligands exhibiting activity against critical targets associated with Alzheimer's disease pathology. By leveraging advanced computational techniques, AlzyFinder streamlines the identification of potential therapeutic agents, thereby expediting the discovery and development of effective treatments for Alzheimer's disease. This platform not only enhances the efficiency of drug discovery processes but also holds promise in advancing our understanding and management of Alzheimer's disease on a molecular level.

To achieve this, AlzyFinder classifies proteins according to the Illuminating the Druggable Genome (IDG) into kinases, G protein-coupled receptors, ion channels, and miscellaneous. We use the OpenTarget platform to identify key targets related to Alzheimer's disease. Subsequently, we employ the ChEMBL database to search for all compounds with reported biological activity, using the pChemBL value as a measure. This integrated approach ensures a comprehensive and targeted strategy in the search for potential Alzheimer's disease treatments and the probability of identifying the possible interactions between different targets.

Designed and managed by Ramirez Lab and University of Concepción.

The authors of the AlzyFinder received public funding from the following funders:

Agencia Nacional de Investigación y Desarrollo (ANID) - Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) – Chile (Grant No. 1220656).

If you make use of the AlzyFinder Platform material in scientific publications, please cite our respective articles:

Valero-Rojas, J.; Ramírez, C.; Pacheco-Paternina, L.; Valenzuela-Hormazabal, P.; Santana, P.; González, J.; Gutiérrez-Bunster, T.; Valdés-Jiménez, A.; Ramírez, D. AlzyFinder: An ML-driven platform for ligand-based virtual screening and network pharmacology. DOI: 10.26434/chemrxiv-2024-2h0fw. ChemRxiv Preprint.

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