ArticleBioinformatics (Oxford, England)2022
A graph neural network approach for molecule carcinogenicity prediction.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.Journal of chemical information and modeling · 2025Guideline
- LMF-CP: An Interpretable Multimodal Late-Fusion Framework for Compound Carcinogenicity Prediction.International journal of molecular sciences · 2026Article
- Carcinogenicity prediction via multi-task learning of cross-organ representations with attention mechanisms.Briefings in bioinformatics · 2026Article
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.Journal of cheminformatics · 2026Article
- Enhancing multi-task in vivo toxicity prediction via integrated knowledge transfer of chemical knowledge and in vitro toxicity information.Journal of cheminformatics · 2025Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deep active learning with high structural discriminability for molecular mutagenicity prediction.Communications biology · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition.Journal of cheminformatics · 2024Article
- Review
- DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction.Journal of cellular and molecular medicine · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMolecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge.
resultsIn this work, we propose CONCERTO, a deep learning model that uses a graph transformer in conjunction with a molecular fingerprint representation for carcinogenicity prediction from molecular structure. Special efforts have been made to overcome the data size constraint, such as multi-round pre-training on related but lower quality mutagenicity data, and transfer learning from a large self-supervised model. Extensive experiments demonstrate that our model performs well and can generalize to external validation sets. CONCERTO could be useful for guiding future carcinogenicity experiments and provide insight into the molecular basis of carcinogenicity. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are available on github at https://github.com/bowang-lab/CONCERTO.
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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.