Evidence map›Paper›PMID 42410640›Full record

ArticleJournal of cheminformatics2026

MINERVA: a public XAI-powered platform advancing multi-target discovery in Alzheimer's disease.

Nicola Gambacorta, Daniela Trisciuzzi, Fabrizio Mastrolorito, Francesco Leonetti, Cosimo D Altomare, Modesto de Candia, Marco Catto, Nicola Amoroso, Fulvio Ciriaco, Orazio Nicolotti

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Nicola GambacortaDepartment of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, 70125, Bari, Italy. nicola.gambacorta1@uniba.it.
Daniela TrisciuzziDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Fabrizio MastroloritoDepartment of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, 70125, Bari, Italy.
Francesco LeonettiDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Cosimo D AltomareDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Modesto de CandiaDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Marco CattoDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Nicola AmorosoDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.
Fulvio CiriacoDepartment of Chemistry, University of Bari Aldo Moro, 70125, Bari, Italy.
Orazio NicolottiDepartment of Pharmacy-Pharmaceutical Sciences, University of Bari Aldo Moro, 70125, Bari, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder involving a complex interplay of interconnected pharmacological targets, a feature that limits the success of traditional single-target medicinal chemistry approaches. Here, we present MINERVA (Multi-target Interactive Network for Explainable Research and Visualization in Alzheimer's disease), a public web-based platform designed to support multi-target drug discovery assisted by eXplainable Artificial Intelligence (XAI) strategies. MINERVA integrates large-scale disease-focused high-quality data comprising as many as 70,960 small molecules annotated across 33 AD relevant targets, taken from ChEMBL and CADRO databases. To capture different levels of pharmacological relevance, four distinct pharmacological thresholds (i.e., 10 μM, 1 μM, 100 nM, and 10 nM) were set to enable the parallel exploration of weak to high-affinity ligand spaces. Independent Balanced Random Forest (BRF) classifiers were trained for each AD target threshold combination using an extended core-substituent fingerprint, which ensures robustness against class imbalance and chemical heterogeneity. MINERVA incorporates a probability binning based domain of applicability (DoA) to quantify prediction reliability and a SHAP-based explainability framework to fairly map fragment-level contributions directly onto chemical structures. MINERVA is freely accessible at https://prometheus.farmacia.uniba.it/minerva/ .Scientific contributionHerein we introduce MINERVA, the first freely accessible platform that combines large-scale AD-related data curation, multi-target prediction, and multi-threshold bioactivity modeling within a fully explainable and user-friendly environment. By enabling transparent, ligand-based exploration of chemical space across multiple AD pathways, MINERVA provides a unique and practical resource for accelerating multi-target drug discovery in the neurodegenerative research area.

Identifiers

PMID42410640
PMCPMC13621634

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.