A biology-based quality-diversity algorithm for drug repurposing in Alzheimer’s disease using automated machine learning
Year: 2026
Type: article
Abstract: BACKGROUND: Alzheimer’s disease (AD) remains a major therapeutic challenge, characterized by high clinical trial failure rates and limited efficacy of current treatments. Drug repurposing offers a faster, lower-risk route to new therapies; however, existing computational approaches often prioritize predictive accuracy over mechanistic novelty and interpretability, both of which are critical for clinical translation. RESULTS: We introduce a quality-diversity Automated Machine Learning (AutoML) framework that integrates biologically informed graph neural network (GNN) embeddings with a MAP-Elites-guided search to discover predictive yet mechanistically distinct therapeutic hypotheses. Drugs and genes are embedded using GraphSAGE and variational graph autoencoders trained on the Alzheimer’s Knowledge Base (AlzKB), with a clustering loss used to anchor known AD entities and define dimensions of biological novelty. In an AD case study using matched ADSP GWAS-derived features, our framework successfully recovered known drug–gene relationships and identified robust consensus candidates across independent validation runs. Most notably, the search consistently prioritized Triclosan—a recently identified environmental risk factor for AD neuroinflammation—and the Ketamine/Quazepam pair, suggesting a model-driven preference for restoring synaptic E/I balance. Furthermore, exploratory leads such as Exemestane and Felodipine were identified in underrepresented biological niches, supported by enrichment in oxidative stress and autophagy pathways. The framework demonstrated high stability across multiple random seeds and a 48% reduction in computational cost compared to standard multi-objective evolutionary baselines. CONCLUSIONS: Beyond AD, this framework offers a generalizable strategy for integrating biomedical knowledge graphs with diversity-enhancing AutoML to accelerate the discovery of mechanistically novel drug candidates across complex polygenic diseases.
Source: BioData Mining
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: EHR-based Genomic Discovery and Implementation, EHR-based Genomic Risk Assessment and Management for Diverse Populations, Meharry Clinical and Translational Research Center (MeTRC), eMERGE IV Northwest: A partnership to evaluate the use of genomic information in the health care of diverse participants, Better Outcomes for Children: Promoting Excellence in Healthcare Genomics to Inform Policy, The Electronic Medical Records and Genomics (eMERGE) Network Phase III - Coordinating Center (U01), VGER, the Vanderbilt Genome-Electronic Records Project, EMERGE III CENTRAL SEQUENCING, GENOTYPING AND INTERPRETATION FACILITY, eMERGE Phase IV Clinical Center at Mass General Brigham, Artificial Intelligence Strategies for Alzheimer's Disease Research
OpenAlex ID: W7143293243