We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. Our model learns drug representations at multiple scales based on a drug-protein interaction network and metadata. Structural properties of compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug combination representations on the cancer cell type we define a synergy scoring function that can inductively score unseen pairs of drugs. Experimental results on the synergy scoring task demonstrate that MOOMIN outperforms state-of-the-art graph fingerprinting, proximity preserving node embedding, and existing deep learning approaches. Further results establish that the predictive performance of our model is robust to hyperparameter changes. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues, out-of-sample predictions can be validated with external synergy databases, and that the proposed model is data efficient at learning.

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain.

Drug discovery is notorious for its low success rates [5]. Despite best research efforts, the majority of drugs fail at early stages of development, even before they enter clinical trials. This phenomenon stems from the inherent complexity of biological systems and our poor understanding of human diseases. To improve that understanding, swaths of data have been generated in recent years. Still, data does not easily translate into knowledge or actionable insights. Here we explore how approaches from the recommendation system domain could help scientists comprehend the ever-growing amount of biomedical facts. The aim of these efforts is to make better drug development decisions, which ultimately result in safe and efficient treatments for patients [3].