HyperPCM: Robust Task-Conditioned Modeling of Drug--Target Interactions
Sprache des Titels:
Englisch
Original Kurzfassung:
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure?activity relationship (QSAR) and
proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize
representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them
to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by
machine learning and deep neural networks, that allow the development of drug?target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug?target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.
Forschungs-