Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization

Accurate prediction of drug–target interactions (DTIs) is critical for accelerating drug repositioning and reducing the cost of pharmaceutical development. Most existing studies frame DTI prediction as a binary task and often neglect the pharmacological action types and the quality of non-interaction data. This study introduces a multi-class classification framework that categorizes interactions into activators, inhibitors, and non-action classes. A novel zero-interaction selection algorithm is proposed, based on weighted drug–drug and protein–protein similarity scores, to improve dataset diversity and reliability. Drug and protein features were extracted from DrugBank, PubChem, and UniProt, and various feature selection and dimensionality reduction techniques—including decision tree, random forest importance scores, principal component analysis (PCA), Autoencoders, and Permutation importance—were evaluated to identify the most informative features for classification. We also compare concatenation-based and convolution-based feature integration strategies and systematically evaluate a range of classifiers, including both feature-based and graph-based models, with special attention to ensemble learning approaches. The concatenation method consistently outperforms convolution, and Histogram-based Gradient Boosting (HGB) achieves the best predictive overall accuracy with an average of 87.90% on the external test set. Meanwhile, HeteroGNN demonstrates more balanced class-wise performance, particularly for underrepresented classes. This work provides a scalable and interpretable framework for computational drug repositioning, supporting faster and more cost-effective identification of therapeutic candidates.