Why Stereochemistry Must Be Explicitly Represented When ML Predicts Binding, Bioactivity, and Selectivity of Complex Natural Products

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Machine-learning models for molecular prediction can use only chemical distinctions that survive data curation and molecular representation. For complex natural products, this constraint is especially important because stereochemical configuration may contribute to molecular identity, biological recognition, selectivity, and measured activity, yet it can be omitted, flattened, inconsistently resolved, or conflated with conformational information during data preparation. This conceptual article examines stereochemistry as an information-preservation problem rather than as a generic argument that three-dimensional representations are always superior. It integrates evidence from natural-product chemistry, stereosensitive molecular learning, three-dimensional fingerprints, geometry-aware graph models, conformer analysis, and cheminformatics pipelines. The central proposed contribution is a representation principle: when the target endpoint can plausibly depend on configuration, a model should preserve stereochemical identity explicitly, distinguish known from unknown or mixed stereochemical states, and be evaluated against stereo-blinded alternatives before claims of stereochemical sensitivity are accepted. Stereo-preserving two-dimensional graphs, geometry-aware representations, and conformer-aware approaches are therefore treated as complementary regimes rather than a hierarchy with one universally preferred method. The argument is bounded by task dependence, conformer uncertainty, assay noise, incomplete stereochemical annotation, and the possibility that some endpoints are effectively stereo-insensitive. Explicit representation is thus proposed as a condition for valid stereo-sensitive inference, not as evidence that stereochemistry will improve every benchmark or that three-dimensional modeling is always necessary. The framework requires endpoint-specific and prospective validation before deployment claims are justified.