Quantum-Informed Molecular Learning for Natural Product Chemical Space Through Electronic Structure, Reactivity, Intermolecular Forces, and Data-Efficient Property Prediction

Download PDF

Natural products occupy chemically heterogeneous, stereochemically rich molecular space in which connectivity alone may not encode the electronic determinants of reactivity, conformational response, or intermolecular recognition. Yet adding quantum-mechanical information indiscriminately can increase computational cost without improving prediction. This original computational theory develops an evidence-bounded account of when and how quantum information should enter molecular learning for natural-product chemical space. It distinguishes explicit quantum descriptors, learned electronic representations, electronic intermediates, three-dimensional equivariant representations, and physics-informed interaction models; separates reactivity from mechanistic proof and intermolecular energy components from binding or pharmacological activity; and treats label fidelity and acquisition cost as model-design variables. The central contribution is the proposed Quantum-Informed Natural Product Learning Theory: quantum information should be allocated according to a demonstrable physics deficit in the baseline representation, the electronic sensitivity of the endpoint, the relevant molecular state and environment, and the uncertainty associated with transfer. Under this view, quantum augmentation is neither a universal prerequisite nor an ornamental feature. It is an adaptive escalation strategy that can move from topology and geometry toward electronic structure, polarization, long-range interactions, or higher-fidelity labels only when justified. The theory further links multi-fidelity learning and active acquisition to the scarcity of expensive labels. Its principal boundaries are chemical-space coverage, reference-method error, representation dependence, stereochemistry, charge and protonation state, nonlocality, distribution shift, and uncertainty calibration. Prospective natural-product-specific benchmarks are required before claims of general predictive superiority or translational readiness.