DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

Treatments targeting complex diseases, such as cancer, frequently lead to acquired drug resistance, due to patient-specific variability. For instance, drugs targeting only one key component of growth or proliferation pathways, may lead to selective pressure and activation of a compensatory mechanism [1], thus making this treatment suboptimal. However, during multi-target inhibition with reduced stringency, drug resistance is less likely. Therefore, the implementation of combination therapy might improve patient treatment as different drugs may target distinct pathways or genes, likely leading to decreased cancer cell survival. In addition to the increased efficacy, combination therapy often reduces toxicity and decreases the likelihood of treatment resistance compared to monotherapy (i.e., single drug) treatments [2]. Due to advancements in high-throughput screening (HTS), the number of drug screening datasets has been growing in recent years. Some examples include the NCI-ALMANAC dataset [3] which contains 103 FDA-approved drugs tested in 60 different cell lines (NCI-60) [4] or the large oncology dataset produced by Merck&Co [5] which is composed of 38 drugs tested in 39 different cell lines from 6 different tissue types.