Comparative Analysis of Negative Sampling Strategies for Polypharmacy Side Effect Prediction

Student: Virko Elizaveta

Supervisor: Boris Novikov

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Enterprise Software Development (Master)

Year of Graduation: 2021

Polypharmacy is the administration of many drugs simultaneously, and it has demonstrated effectiveness in the treatment of many complex diseases but has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for newly developed drugs. This research shows that the current knowledge graph (KG) based state-of-the-art approach to polypharmacy side effect prediction does not work well for drugs with a limited number of known assertions. This work examine the problem of negative examples generation and show that negative sampling can improve the model prediction without lowering its overall performance.

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