Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction

Drug - drug interactions ( DDIs) account for over 30% of all adverse drug reactions ( ADRs) ca ses and often occur when co - medicate more than two drugs. More alarmingly, it stays a significant ADR - mediated morbidity every year [1], and this ramps up withdrawn - risks of a drug from the market and thu s pulls a strong disincentive to drug development [2] . Though it is ideal for detecting all negative DDIs during clinical trials, DDIs - induced - ADRs cases are often reported at clinical uses and post - marketing surveillance, which pose a severe threat to public health. A study concerning the relationship between DDIs and the mortality rate of elderly hospitalized patients concludes that over 62.77% of patients present at least one DDI, and this may amount strictl y to the death of these patients [3] . Besides, DDIs also expand the length of stay and cost of hospitalization [4] .