Adverse Drug Reaction Prediction via Network Representation Learning and Multimodal Embedding

Date: 22nd October 2021

Time: 14:00-16:00

Location: IF 1.16 - Hybrid Meeting

Talks

Title: Adverse Drug Reaction Prediction via Network Representation Learning and Multimodal Embedding
Speaker: Lauren DeLong
Abstract: 

Novel drugs often fail in late-stage clinical trials due to unforeseen adverse side effects which deem them unsafe for distribution. Many computational approaches have aimed to address this issue, but few have attempted to use Network Representation Learning (NRL) algorithms. Some previous approaches take advantage of the concept that similar chemical composition between drugs, should, in turn, indicate similar biochemical function and therefore similar side effects. However, this principle is shown to be violated frequently in clinical practice, such as in the case of Procaine and Procainamide, a local anesthetic and an antiarrhythmic, respectively. In contrast, newer approaches show improved performance with an increased variety of biomedical information. The complex landscape of interactions between drugs, targets, protein-protein interactions and adverse drug reactions (ADRs) can be modeled as a large heterogeneous graph in which nodes represent proteins, drugs, or ADRs, and edges which exist between these varieties of nodes indicate some sort of interaction, such as that a certain drug is associated with a side effect. Network topological measures can provide insight into which drugs are more likely to result in adverse events than others. However, in the context of drug development it is essential to de-risk targets and compounds as early as possible. Hence, it is important to predict adverse events for compounds and unwanted phenotypes for targets. These two problems can be formulated as link prediction tasks in the heterogeneous graph. Technically, this can be addressed via NRL, where an encoder is used to find low dimensional node embeddings and a corresponding decoder is used to assign likelihoods to each of the possible edges in the network. In this work, the Relational Graph Attention Network is extended to operate on multimodal biological input and compared alongside previously established side effect prediction methods to evaluate the efficacy of deep NRL for side effect prediction.