A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions

Abstract

Drug Target Interactions (DTIs) are crucial in drug discovery as it reduces the range of candidate searches, speeding up the drug screening process. Considering in vitro and in vivo experimentations are time and cost-expensive, there has been a surge in computational techniques, especially ML methods for DTIs prediction. Therefore, this study aims to present a methodology that uses molecular structures and amino acid sequences for generating PSSM and PubChem fingerprints for drugs and targets respectively. The proposed work uses a novel technique NearestCUS for handling the class imbalance problem of the benchmark datasets. We use Isomap Embedding to extract features from PSSMs. Feature selection is performed using ANOVA. CatBoost is used for predicting the interaction between drugs and targets for the first time. To quantify the efficacy of NearestCUS, we compared it with other sampling techniques. We found that the proposed methodology performed better than state-of-the-art approaches.

Keywords: ANOVA; CatBoost; DTI prediction; clustering-based undersampling; isomap.