Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation

Oliver Lloyd*, Yi Liu, Tom R Gaunt

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Motivation
Adverse reactions from drug combinations are increasingly common, making their accurate prediction a crucial challenge in modern medicine. Laboratory-based identification of these reactions is insufficient due to the combinatorial nature of the problem. While many computational approaches have been proposed, tensor factorisation models have shown mixed results, necessitating a thorough investigation of their capabilities when properly optimized.

Results
We demonstrate that tensor factorisation models can achieve state-of-the-art performance on polypharmacy side effect prediction, with our best model (SimplE) achieving median scores of 0.978 AUROC, 0.971 AUPRC, and 1.000 AP@50 across 963 side effects. Notably, this model reaches 98.3% of its maximum performance after just two epochs of training (approximately 4 minutes), making it substantially faster than existing approaches while maintaining comparable accuracy. We also find that incorporating monopharmacy data as self-looping edges in the graph performs marginally better than using it to initialize embeddings.

Availability and Implementation
All code used in the experiments is available in our GitHub repository (https://doi.org/10.5281/zenodo.10684402). The implementation was carried out using Python 3.8.12 with PyTorch 1.7.1, accelerated with CUDA 11.4 on NVIDIA GeForce RTX 2080 Ti GPUs.
Original languageEnglish
Article numberbtae706
JournalBioinformatics
Volume40
Issue number12
Early online date25 Nov 2024
DOIs
Publication statusE-pub ahead of print - 25 Nov 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024. Published by Oxford University Press.

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