Multimodal Molecular Intelligence for Natural Product Drug Discovery: Integrating Chemical Structures, Bioassays, Omics Profiles, Text Evidence, and Clinical Signals

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Natural product drug discovery increasingly draws on heterogeneous evidence spanning chemical structures, biological assays, molecular profiles, published literature, and clinical observations, yet these evidence streams are commonly stored, modelled, and interpreted in isolation. This article proposes a multimodal molecular intelligence framework for organizing and integrating such evidence without treating computational convergence as proof of mechanism, efficacy, safety, or clinical utility. The framework begins with traceable natural product identity and botanical-source records, followed by complementary representations of chemical structures, stereochemistry, molecular descriptors, fingerprints, and molecular graphs. Bioassay evidence is incorporated with assay metadata, exposure context, target annotations, and explicit confidence boundaries. Transcriptomic, proteomic, metabolomic, genomic, microbiomic, pathway, and network evidence are positioned as context-dependent sources of mechanistic hypotheses. Literature mining, named entity recognition, relation extraction, knowledge graphs, and citation traceability provide structured text evidence while preserving source provenance. Clinical literature, real-world observations, pharmacovigilance reports, adverse-event information, ADMET evidence, and toxicity signals contribute translational and safety context but are not treated as causal confirmation. The proposed architecture combines modality alignment, cross-modal consistency assessment, conflict detection, missing-data handling, uncertainty estimation, bias assessment, expert review, candidate-prioritization boundaries, validation planning, and iterative feedback. Its principal contribution is a provenance-preserving and validation-gated framework that supports research prioritization while maintaining explicit no-clinical-claim boundaries.