Deep Learning for Natural Product Bioactivity Prediction: A Critical Review of Molecular Representation, Model Reliability, and Therapeutic Relevance

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Natural products remain a major source of chemically diverse and pharmacologically informative molecules, yet their structural complexity, sparse annotation, assay heterogeneity, and uneven database coverage create persistent barriers for computational bioactivity prediction. Deep learning has expanded the analytical toolkit for natural product research by enabling representation learning from molecular structures, bioactivity records, genomic context, and drug-discovery datasets. This critical review evaluates how deep learning has been applied to natural product bioactivity prediction, with particular attention to molecular representation, feature learning, model performance, reliability, reproducibility, and therapeutic relevance. The review critically appraises evidence across natural product databases, deep neural networks, graph-based models, SMILES-based models, transformer-style architectures, biosynthetic-gene-cluster learning, drug–target interaction prediction, ADMET prediction, uncertainty estimation, explainability, and reproducible computational workflows. The synthesis emphasizes that descriptors, fingerprints, SMILES strings, molecular graphs, and learned embeddings do not merely encode molecules differently; they shape the kinds of structure–activity relationships that models can detect and the errors that may remain hidden. The review also distinguishes retrospective benchmark performance from pharmacological meaning, arguing that predicted bioactivity should be interpreted as prioritization or hypothesis generation rather than therapeutic evidence. Major limitations include dataset imbalance, benchmark leakage, activity cliffs, limited external validation, weak interpretability, incomplete reproducibility, and uncertain translation from predicted activity to biological mechanism or therapeutic value. Future progress requires natural product-specific benchmarks, leakage-aware validation, uncertainty reporting, interpretable modeling, reproducible workflows, and integration with experimental pharmacology, medicinal chemistry, toxicology, and translational validation.