Generative Design of Natural Product-Like Molecules: Constraint-Guided Optimization for Bioactivity, Safety, Synthetic Accessibility, and Developability

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Generative artificial intelligence provides a computational basis for exploring molecular structures beyond conventionally enumerated chemical libraries, but unconstrained generation can produce structures that are chemically unrealistic, biologically unsupported, unsafe, difficult to synthesize, or unsuitable for further development. This article proposes a conceptual generative AI architecture for designing natural product-like molecules under explicit chemical, biological, safety, synthesis, and developability constraints. The approach distinguishes natural product-like molecules from isolated natural products and treats natural product-likeness as a multidimensional design characteristic rather than evidence of origin, bioactivity, or therapeutic value. The architecture integrates provenance-aware data curation, stereochemistry-sensitive molecular representations, scaffold- and analogue-oriented generation, chemical-validity controls, natural product-likeness and diversity constraints, target-context and bioactivity prediction, selectivity considerations, ADMET and toxicity filters, synthetic-accessibility assessment, retrosynthetic plausibility, reaction-feasibility review, uncertainty estimation, and expert curation. Multi-objective scoring and Pareto-style prioritization are proposed to expose trade-offs rather than conceal them within a single composite measure. Developability filters address physicochemical properties, solubility, permeability, stability, metabolic liabilities, and formulation-relevant concerns while preserving explicit decision boundaries. The principal contribution is a structured design–filter–review–validate architecture that positions generated structures as testable molecular hypotheses. Synthesis, analytical characterization, biological evaluation, experimental ADMET assessment, toxicity testing, and subsequent development studies remain necessary before stronger pharmacological, safety, or translational claims can be made.