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September 23, 2025

A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.

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Review reveals advances in deep learning for drug-target binding in drug discovery, suggesting future directions.

Deep learning significantly enhances drug-target binding predictions, streamlining drug discovery processes.
Recent models, such as graph-based and attention-based architectures, provide innovative approaches for predicting drug-target interactions.
A thorough analysis of benchmark datasets reveals gaps in current evaluation metrics for drug-target binding studies.
Case studies demonstrate the potential of deep learning in identifying key drug-target relationships, especially in cancer research.

Debnath et al. (2025) studied this question.

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