Machine Learning Approaches for Compound–Target Interaction Prediction: A Review

Compound-target interaction (CTI) prediction plays a critical role in drug discovery and the functional study of food-derived bioactive compounds. However, traditional experimental methods for CTI identification are limited by high costs, long cycle times, and high false-positive rates, highlighting an urgent need for more efficient approaches. Machine learning (ML) has become a revolutionary tool to address these challenges. In this review, we focus on recent developments in ML-based CTI prediction. We first systematically outline the commonly used public databases and feature extraction methods for both compounds (molecular fingerprints) and proteins (sequence-derived features), followed by elaborating on four types of ML approaches, including classical supervised learning, matrix factorization, graph topology-based inference, and deep neural network frameworks. In particular, this review explores the emerging application of these computational approaches in identifying targets of food-derived bioactive compounds, underscoring its significant potential to advance functional food research. Moreover, we analyze key challenges, such as limited model interpretability, high data dependency, and insufficient multi-source information integration, and put forth future prospects to improve the prediction of food-derived CTIs, thereby facilitating their application in functional food research.