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Cytochrome P450 2D6 ( CYP2D6 ) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in  CYP2D6  are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making  CYP2D6  one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to  CYP2D6  haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of  CYP2D6  haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts  CYP2D6  haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in  in vitro  functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

Transfer learning enables prediction of CYP2D6 haplotype function