iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with
Prediction of new drug-target interactions is extremely important as it can lead the re-
searchers to find new uses for old drugs and to realize the therapeutic profiles or side effects
thereof. However, experimental prediction of drug-target interactions is expensive and time-
consuming. As a result, computational methods for prediction of new drug-target interactions
have gained much interest in recent times.
We present iDTI-ESBoost, a prediction model for identification of drug-target interactions
using evolutionary and structural features. Our proposed method uses a novel balancing
technique and a boosting technique for the binary classification problem of drug-target in-
teraction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost
outperforms the state-of-the-art methods in terms of area under Receiver operating charac-
teristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing
method in the literature to-date in terms of area under precision recall (auPR) curve. This
is significant as auPR curves are argued to be more appropriate as a metric for comparison
for imbalanced datasets, like the one studied in this research. In the sequel, our experiments
establish the effectiveness of the classifier, balancing methods and the novel features incor-
porated in iDTI-ESBoost.
iDTI-ESBoost is a novel prediction method that has for the first time exploited the struc-
tural features along with the evolutionary features to predict drug-protein interactions. We
believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR
would motivate the researchers and practitioners to use it to predict drug-target interactions.
To facilitate that, iDTI-ESBoost is readily available for use at: http://farshidrayhan.