ADME-DTI: Augmented Deep Meta Ensemble for Drug-Target Interaction Prediction

Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identifying and analyzing these interactions is often resource-intensive, requiring extensive experimentation to evaluate the binding relationships between numerous drugs and their respective targets. This complexity is further compounded by the fact that a drug can inhibit multiple targets, and a target may also bind to various drugs. To address these issues, advanced deep learning models have been introduced as promising tools, offering the ability to accurately predict binding affinity and other bioactivity values to distinguish between potential drug-target interactions. The proposed model, named the Augmented Deep Meta Ensemble for Drug-Target Interaction (ADME-DTI), leverages multiple descriptors and fingerprint representations to extract meaningful insights from drug and protein data. These submodels from each representation are then combined with a deep learning architecture along with the metadata of the drug and target entries. The proposed approach has proved competitive performance against state-of-the-art models across diverse datasets and evaluation metrics, as evidenced by key metrics, namely $r_m^2$ , concordance index, and mean squared error. Specifically, the model achieved mean squared error values of 0.186 (Davis), 0.118 (Kiba), 0.134 (DTC), 0.262 (Metz), 0.300 (ToxCast), and 0.791 (STITCH), all of which are publicly available benchmark datasets commonly used in drug-target interaction prediction tasks. The results highlight the model's effectiveness in reducing prediction errors and improving accuracy. Previous drug-target interaction research often relied on limited, non-diverse datasets, reducing generalizability. Few studies addressed drug-target interaction prediction as a regression task. Our novel deep learning metamodel integrates multiple models and representations, surpassing benchmarks and delivering greater prediction accuracy across varied datasets and evaluation metrics.