Sparse Labels, Rich Chemistry: Building Few-Shot Learning Strategies for Bioactivity Prediction Across Structurally Diverse and Data-Limited Natural Product Chemical Space

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Natural products occupy chemically diverse, provenance-rich regions of molecular space, yet experimental bioactivity labels are often sparse, unevenly distributed, and assembled from heterogeneous assay sources. Conventional small-data modeling treats this primarily as a sample-size problem. This article develops an original methodological framework in which few-shot natural-product bioactivity prediction is instead treated as a joint problem of task definition, chemical-space coverage, representation choice, support-set design, transferability, leakage-controlled evaluation, and uncertainty management. The framework distinguishes scarcity of labels from reliability of labels, rarity of scaffolds from pharmacological novelty, and predictive similarity from mechanistic equivalence. It further argues that molecular representation should be selected empirically rather than hierarchically: fingerprints and descriptors remain necessary low-data controls, while self-supervised and foundation-scale representations are candidates whose value depends on source–target alignment and target-like validation. Support examples are conceptualized as part of the learning design rather than passive training observations, and subsequent adaptation must be conditional on task relatedness. The principal contribution is a proposed strategy for deciding when few-shot learning is scientifically informative in heterogeneous natural-product space and when abstention, additional measurement, or non-transfer baselines are preferable. The framework is not a validated performance standard and does not establish prospective predictive superiority, mechanistic correctness, or translational readiness. Its value lies in converting low-label prediction from an architecture-selection problem into an evidence-bounded design problem whose assumptions can be explicitly tested.