In silico screening of multi-target drugs against Alzheimer’s Disease: a repurposing approach
Mariana Bertoldi Amato, Daniela Peres Martinez, Rafaella Sinnott Dias, Fabiane Neitzke Höfs, Frederico Schmitt Kremera
Abstract
Alzheimer’s Disease (AD) is characterized by complex pathophysiology involving beta-amyloid plaques and neurofibrillary tangles, for which current therapies have limited efficacy. To address the multifactorial nature of AD, this study investigated the hypothesis that existing FDA-approved drugs can be repurposed as multi-target agents by computationally screening for simultaneous activity against key pathological drivers. We developed a pipeline of predictive Quantitative Structure-Activity Relationship (QSAR) models for critical AD-related targets, including AChE, BACE1, and proteins involved in tau pathology. The methodological rigor of these models, which primarily leveraged the Extremely Randomized Tree algorithm, was ensured through robust validation techniques including cross-validation, y-randomization, and a thorough applicability domain analysis. Our virtual screening identified several FDA-approved drugs, notably antipsychotics like Droperidol and Pimozide, as high-potential multi-target candidates. Subsequent molecular docking analysis revealed plausible binding modes for these candidates within the active sites of key targets. While these computational results suggest promising mechanisms for inhibiting multiple disease pathways, they require definitive experimental validation to confirm their effectiveness. By shifting the focus from automation to insight, this work provides a validated computational framework that generates testable hypotheses for drug repurposing in AD and prioritizes specific, clinically-approved molecules for future investigation into their potential multi-target efficacy.
Supplementary material accompanies this paper:
Keywords
References
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Submitted date:
07/23/2025
Accepted date:
01/03/2026
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