Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
TL;DR
This work tackles dose-response prediction for cancer drug combinations by proposing a permutation-invariant multi-output Gaussian process (PIMOGP) with a scalable variational inference framework that provides uncertainty quantification and handles missing data. It integrates a deep generative model to encode chemical space via SELFIES, enabling continuous representations for novel drugs and combinations. The model employs a linear model of coregionalisation (LMC) to capture cross-output dependencies and enforces permutation invariance across drug order, with inference performed through stochastic variational inference (SVI). On a high-throughput dataset, the approach achieves strong cross-output information sharing and competitive predictive performance, while delivering uncertainty estimates and a framework extensible to new molecules and cell lines.
Abstract
Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental design or inform treatment decisions. Building on previous work that makes use of permutation invariant multi-output Gaussian Processes in the context of dose-response prediction for drug combinations, we develop a variational approximation to these models. The variational approximation enables a more scalable model that provides uncertainty quantification and naturally handles missing data. Furthermore, we propose using a deep generative model to encode the chemical space in a continuous manner, enabling prediction for new drugs and new combinations. We demonstrate the performance of our model in a simple setting using a high-throughput dataset and show that the model is able to efficiently borrow information across outputs.