Predictive accuracy of peptide–target interaction models in drug discovery: a systematic review and meta-analysis
Background Peptides represent promising therapeutic agents due to their high specificity, biocompatibility, and capacity to modulate protein–protein interactions. However, the field faces critical challenges: inconsistent evaluation metrics, heterogeneous datasets, and poor reproducibility, which together undermine objective model comparison and benchmarking. Objectives To systematically review and quantitatively assess the predictive performance of machine learning (ML)-based peptide–target interaction (PTI) models, with emphasis on commonly reported metrics including area under the curve (AUC), concordance index (CI), and Precision. Methods We conducted a systematic literature search across PubMed, arXiv, and Cochrane databases for studies published through 1 August 2025. Inclusion criteria required original ML-based peptide–target prediction models with quantitative performance metrics. Results Twenty-three studies met inclusion criteria. Fourteen reported AUC values (pooled estimate: 0.87, 95% Confidence Interval: 0.83–0.90), five reported Concordance Index values (0.90, 95% Confidence Interval: 0.89–0.91), and six reported precision (0.75, 95% Confidence Interval: 0.69–0.81). Top-performing models predominantly employed transformer or graph neural network architectures with structural input features. Critical limitations included inconsistent reporting practices, infrequent external validation, and limited data/code availability. The observation that the twenty-three studies present challenges to a pooled estimate is itself a consequence of the reporting practices we document, and is why we advance STRIDE. Conclusion ML models demonstrate strong potential for PTI prediction, with leading approaches achieving robust classification and ranking performance. Nevertheless, progress is hindered by non-standardised evaluation metrics, limited transparency, and insufficient reproducibility. We introduce the STRIDE framework (encompassing Standardization, Transparency, Representativeness, Integration, Discovery, and Evidence) to establish rigorous evaluation standards, enhance methodological reproducibility, and support future evaluation of clinical applicability.