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…