NDDSA: A network- and domain-based method for predicting drug-side effect associations

摘要

Finding side effects of drugs, before reaching the animal and clinical test, can decrease the cost and time of developing new drugs. Also, a lot of side effects are reported after going to market and using it by patients (post-marketing). There are some systems to report these events but most of the time, side effects stay hidden. Thus, a lot of computational methods have been developed to predict the side effects of drugs before reaching the clinical test or going to market. These methods are based on different features of drugs like chemical or biological features. Most of these methods can just complete the side effect list of approved drugs or require a great deal of biological information about the drugs. So, it is impossible to find the side effects of a new drug candidate very early. In this paper, we propose a network and domain-based algorithm (NDDSA) that can predict the side effects of new drug candidates by assigning a score to each side effect. Unlike other methods, it needs just the chemical formula of the new drug. We show that NDDSA performs better than the best present method in case of predicting ADRs for a new drug and has acceptable results in case of predicting potential side effects for approved drugs, while it is much faster than the other methods.