A graph neural network approach for molecule carcinogenicity prediction

Philip Fradkin, Adamo Young, Lazar Atanackovic, Brendan Frey, Leo J Lee, BO WANG

Abstract:

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.