Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies

Drug discovery is a long, resource-intensive process with high failure rates. Traditional experimental identification of drug–target interactions (DTIs) is especially time-consuming and costly. This comprehensive review examines how Artificial Intelligence (AI) and Machine Learning (ML) are transforming DTI prediction, offering substantial potential to reduce drug development time and costs. The review provides a detailed examination of AI/ML-based techniques, detailing data representations for drugs, targets, and their interactions through joint drug–target processing. The review extensively discusses feature extraction and engineering methods, including the construction of interaction-specific features. It also encompasses a broad range of learning paradigms and model architectures, including supervised learning, advanced Graph Neural Networks (GNNs), Deep Learning (DL) models, and hybrid approaches. We further examine training protocols and robust evaluation metrics crucial for assessing model generalization. Ultimately, this review highlights the capacity of these advanced AI/ML methods to deliver more accurate, scalable, and interpretable solutions for DTI prediction. This is crucial for accelerating key stages of drug development, including lead compound identification, off-target profiling, drug repurposing, polypharmacology analysis, and the realization of precision medicine.