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A graph neural network approach for molecule carcinogenicity prediction
Philip Fradkin1,2, Adamo Young2,3, Lazar Atanackovic1,2
1Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
Motivation:
Molecular 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.
Results:
In 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.
Insights
Identifying carcinogenic compounds is challenging due to expensive testing. CONCERTO, a deep learning model, predicts carcinogenicity from molecular structure, overcoming data limitations for better cancer prevention insights.
Area of Science:
- Computational chemistry
- Toxicology
- Machine learning
Background:
- Identifying molecular carcinogenicity is crucial for cancer prevention but hindered by costly, low-throughput animal testing.
- Limited carcinogenicity data presents a significant challenge for developing accurate predictive models.
Purpose of the Study:
- To develop an efficient and accurate deep learning model for predicting molecular carcinogenicity from chemical structures.
- To address data scarcity issues in carcinogenicity prediction through advanced machine learning techniques.
Main Methods:
- Proposed CONCERTO, a deep learning model integrating graph transformers and molecular fingerprints for structure-based carcinogenicity prediction.
- Employed multi-round pre-training on mutagenicity data and transfer learning from self-supervised models to overcome data size constraints.
Main Results:
- CONCERTO demonstrates strong performance and generalizability on external validation datasets.
- The model effectively predicts carcinogenicity from molecular structure, offering a viable alternative to traditional methods.
Conclusions:
- CONCERTO provides a powerful tool for predicting molecular carcinogenicity, potentially guiding experimental design and reducing costs.
- The model offers insights into the molecular mechanisms underlying carcinogenicity, aiding in the development of safer chemicals.
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