Molecular Foundation Models for Phytochemical Space: Encoding Natural Product Structure, Bioactivity, Target Context, and Therapeutic Potential

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Molecular foundation models are emerging as general-purpose representation systems for chemical and biological prediction, yet phytochemical space remains poorly served by models trained mainly on generic small-molecule data. This article proposes a conceptual framework for molecular foundation models designed specifically to represent natural product complexity without converting computational predictions into unsupported therapeutic claims. The framework organizes phytochemical information across identity, source, chemical structure, stereochemistry, scaffold architecture, biosynthetic context, molecular graphs, molecular language, three-dimensional information where supported, bioactivity evidence, assay context, target and pathway relationships, safety signals, provenance, data quality, and uncertainty. It distinguishes representation learning from evidence interpretation by separating pretraining, transfer learning, multimodal integration, and fine-tuning from the validation steps required to establish bioactivity, target engagement, safety, or therapeutic usefulness. Target-context encoding is positioned as a means of generating testable hypotheses and prioritizing experiments, while therapeutic potential is treated as a bounded research construct rather than a model-confirmed property. The proposed design requirements emphasize natural product-aware training data, stereochemistry-sensitive encoders, assay-aware labels, provenance tracking, calibrated uncertainty, bias assessment, external validation, experimental confirmation, expert curation, and transparent reporting. The main contribution is an original, evidence-grounded architecture for aligning molecular foundation model development with the distinctive informational structure of phytochemical space and with responsible translational interpretation.