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Knowledge Graph Completion to Predict Polypharmacy Side Effects

arXiv.org Machine Learning

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.


Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

arXiv.org Machine Learning

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by 3$\times$.


Drug-Drug Adverse Effect Prediction with Graph Co-Attention

arXiv.org Machine Learning

Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such accidents have been affecting an increasing proportion of the population (15% in the US now) and it is thus of high interest to be able to predict the potential side effects as early as possible. Systematic combinatorial screening of possible drug-drug interactions (DDI) is challenging and expensive. However, the recent significant increases in data availability from pharmaceutical research and development efforts offer a novel paradigm for recovering relevant insights for DDI prediction. Accordingly, several recent approaches focus on curating massive DDI datasets (with millions of examples) and training machine learning models on them. Here we propose a neural network architecture able to set state-of-the-art results on this task---using the type of the side-effect and the molecular structure of the drugs alone---by leveraging a co-attentional mechanism. In particular, we show the importance of integrating joint information from the drug pairs early on when learning each drug's representation.


A Unified View of Relational Deep Learning for Drug Pair Scoring

arXiv.org Artificial Intelligence

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.


Stanford researchers develop AI that can predict pharmaceutical drug interactions

#artificialintelligence

For many Americans, taking prescription pills is a part of a never-ending routine. About seven out of ten people take at least one drug, according to the Mayo Clinic, and as many as 15 percent take five or more. The trouble is, it's difficult to predict the side effects that might result from polypharmacy, or the practice of combining drugs; by some estimates, there are more than 125 billion potential complications. "It's practically impossible to test a new drug in combination with all other drugs, because just for one drug that would be five thousand new experiments," Marinka Zitnik, a postdoctoral fellow in computer science at Stanford, said. "[T]ruly, we don't know what will happen."