Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

Abstract

Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

Review of Predicting Synergistic Drug Combinations

“…It also provides relevant information about the indications for the drug. Prinz, J. et al [ 48 ] proposed a novel machine-learning approach that combines data from SIDER and GWASdb databases into a joint matrix. The model could be used to develop treatments with fewer side effects and test new indications for existing drugs.…”