Microbial genome mining routinely identifies far more biosynthetic gene clusters (BGCs) than can be experimentally characterized, creating a prioritization problem rather than a simple detection problem. Sequence novelty is frequently treated as the principal triage signal, yet sequence divergence does not guarantee production, structural novelty, ecological relevance, or practical chemical access. This article develops an original computational prioritization model that integrates four non-equivalent evidence domains: genomic novelty and biosynthetic interpretability, metabolite evidence, ecological context, and chemical tractability with rediscovery risk. The model is designed as evidence-aware decision support rather than a validated predictor of discovery success. It distinguishes encoded biosynthetic potential from observed chemistry, candidate BGC–metabolite associations from structural or causal assignments, and ecological plausibility from demonstrated function. It further treats tractability as an independent consideration because scientifically unusual clusters may remain silent, analytically masked, difficult to activate, or costly to isolate, whereas readily observable chemistry may be dominated by known products. The proposed architecture therefore favors convergent evidence, records provenance and uncertainty, and permits candidate priority to change as orthogonal evidence accumulates. Its intended use is to allocate experimental effort more transparently across microbial natural-product programs while preserving alternative explanations and failure states. The framework does not establish universal weights, calibrated probabilities of novel chemistry, or prospective superiority over existing workflows; those claims require class-diverse, taxonomically broad, prospective validation.
