Unveiling molecular moieties through hierarchical Grad-CAM graph explainability

Background Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence for their ability to model complex molecular structu...

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Vydáno v:	BMC bioinformatics Ročník 26; číslo 1; s. 261 - 23
Hlavní autoři:	Contino, Salvatore, Sortino, Paolo, Gulotta, Maria Rita, Perricone, Ugo, Pirrone, Roberto
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 23.10.2025 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Artificial intelligence Binding Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biological activity Biomedical and Life Sciences Classification Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer-aided design Computer-Aided Drug Design (CADD) Datasets Drug development Drug discovery Drug Discovery - methods Electronic data processing Graph Explainability Graph Neural Networks Graph representations Graphical representations Kinases Life Sciences Ligands Message passing Methods Microarrays Molecular moieties Molecular structure Molecules Neural networks Neural Networks, Computer Optimization Prediction models Proteins Screening Technology application Virtual Screening Italy Computer-Aided Drug Design (CADD) Graph Explainability Virtual Screening Molecular moieties Graph Neural Networks
ISSN:	1471-2105, 1471-2105
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Popis
Shrnutí:	Background Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence for their ability to model complex molecular structures using graph-based representations. However, the integration of explainable methods to elucidate the specific contributions of molecular substructures to biological activity remains a significant challenge. This limitation hampers both the interpretability of predictive models and the rational design of novel therapeutics. Results We trained 20 GNN models on a dataset of small molecules with the goal of predicting their activity on 20 distinct protein targets from the Kinase family. These classifiers achieved state-of-the-art performance in virtual screening tasks, demonstrating high accuracy and robustness on different targets. Building upon these models, we implemented the Hierarchical Grad-CAM graph Explainer (HGE) framework, enabling an in-depth analysis of the molecular moieties driving protein-ligand binding stabilization. HGE exploits Grad-CAM explanations at the atom, ring, and whole-molecule levels, leveraging the message-passing mechanism to highlight the most relevant chemical moieties. Validation against experimental data from the literature confirmed the ability of the explainer to recognize a molecular pattern of drugs and correctly annotate them to the known target. Conclusions Our approach may represent a valid support to shorten both the screening and the hit discovery process. Detailed knowledge of the molecular substructures that play a role in the binding process can help the computational chemist to gain insights into the structure optimization, as well as in drug repurposing tasks.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-025-06208-y