ArticleJournal of cellular and molecular medicine2023
DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction.
Article in Journal of cellular and molecular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.Journal of chemical information and modeling · 2025Guideline
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- LMF-CP: An Interpretable Multimodal Late-Fusion Framework for Compound Carcinogenicity Prediction.International journal of molecular sciences · 2026Article
- RGPA-GCN: Graph convolutional networks for rice gene-phenotype association prediction.Plant biology (Stuttgart, Germany) · 2026Article
- AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2026Article
- Carcinogenicity prediction via multi-task learning of cross-organ representations with attention mechanisms.Briefings in bioinformatics · 2026Article
- Capsule enclosed coordinate attention based dual batch depthwise convolutional knowledge distillation model for drug-drug interaction prediction.Molecular diversity · 2026Article
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Exploring necroptosis-associated genes: implications for immune responses and therapeutic strategies in diabetic foot ulcers.Archives of medical science : AMS · 2026Article
- Graph attention networks-based prediction of MicroRNA-disease causality in head and neck neoplasms.Scientific reports · 2025Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- MVSGDR: multi-view stacked graph convolutional network for drug repositioning.Briefings in bioinformatics · 2025Article
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- Prediction of cardiovascular diseases based on GBDT+LR.Scientific reports · 2025Article
- A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- CNV-mediated dysregulation of the ceRNA network mechanism revealed heterogeneity in diffuse and intestinal gastric cancers.Journal of translational medicine · 2025Article
- A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.Journal of cellular and molecular medicine · 2025Article
- Intratumoral Heterogeneity and Immune Microenvironment in Hepatoblastoma Revealed by Single-Cell RNA Sequencing.Journal of cellular and molecular medicine · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.
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Registered trials
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.