What type of study is this?

This is a Literature Review study.

What question did this study set out to answer?

This review examines the role of machine learning in predicting compound–target interactions (CTIs) within drug discovery.

May 6, 2026FoodsOpen Access

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

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Authors

JZJingjie ZhangEast China University of Science and Technology TLTengyu LiBeijing Technology and Business University CYChi YanBeijing Technology and Business University

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Implication

Review explores machine learning methods for predicting interactions of bioactive compounds and proteins, indicating significant advances in drug discovery.

Key Points

This review examines the role of machine learning in predicting compound–target interactions (CTIs) within drug discovery.
Outlined public databases and feature extraction methods for compounds and proteins.
Discussed four ML approaches: supervised learning, matrix factorization, graph topology-based inference, and deep neural networks.
Analyzed challenges such as model interpretability, data dependency, and information integration.
Identified significant potential in using ML for enhancing CTI prediction in functional food research.
Outlined the limitations of current CTI identification methods and suggested improvements.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69faa2b504f884e66b5334fc https://doi.org/10.3390/foods15091582

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Implication

Review explores machine learning methods for predicting interactions of bioactive compounds and proteins, indicating significant advances in drug discovery.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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