MINERVA: a public XAI-powered platform advancing multi-target discovery in Alzheimer's disease
Nicola Gambacorta1,2, Daniela Trisciuzzi3, Fabrizio Mastrolorito4,3
1Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, 70125, Bari, Italy. nicola.gambacorta1@uniba.it.
Journal of Cheminformatics
|July 7, 2026
Summary
Alzheimer's disease drug discovery is advanced by MINERVA, a new platform using explainable AI for multi-target analysis. It integrates extensive data to accelerate the search for novel neurodegenerative treatments.
Area of Science:
- Neuroscience and Pharmacology
- Computational Chemistry
- Artificial Intelligence in Drug Discovery
Background:
- Alzheimer's disease (AD) is complex, involving multiple targets, which challenges traditional single-target drug discovery.
- Existing approaches often fail due to the multifactorial nature of AD.
Purpose of the Study:
- To introduce MINERVA, a public web-based platform for multi-target drug discovery in Alzheimer's disease.
- To leverage explainable artificial intelligence (XAI) for enhanced drug discovery strategies.
Main Methods:
- MINERVA integrates large-scale data (70,960 small molecules, 33 AD targets) from ChEMBL and CADRO.
- Four pharmacological thresholds (10 μM to 10 nM) were used to explore ligand spaces.
- Balanced Random Forest classifiers and SHAP-based explainability were employed for robust predictions and structural insights.
Main Results:
- The platform provides a comprehensive resource for exploring chemical space across multiple AD pathways.
- It enables transparent, ligand-based analysis of potential drug candidates.
- MINERVA quantifies prediction reliability using a domain of applicability (DoA).
Conclusions:
- MINERVA is the first platform combining AD data curation, multi-target prediction, and explainability for drug discovery.
- It offers a user-friendly environment to accelerate the development of multi-target therapeutics for neurodegenerative diseases.
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