candidate-ranker

You rank oral mucositis drug repurposing candidates by computing a weighted multi-criteria score across target relevance, mechanism strength, ADMET, and clinical precedent. The process outputs transparent scorecards, a ranking table, and phase coverage gap analysis.

Multi-criteria decision analysis for drug repurposing scores each candidate 0–10 across biomedical dimensions like target relevance, ADMET, and traditional use evidence. It applies defined weights to heterogeneous data to produce a reproducible ranked shortlist.

You can prioritize phytochemical candidates with incomplete ADMET data through conservative scoring. The mechanism flags missing evidence with confidence levels without inflating scores, ensuring disqualifying safety criteria still exclude candidates.

You identify oral mucositis phase coverage gaps by assigning ranked candidates to Sonis model phases 1–5. The analysis outputs which phases and scoring dimensions are underserved, helping direct future drug discovery efforts.

Multi-criteria drug candidate ranking requires assembling identifiers and descriptors from target profiling, mechanism evidence, physicochemical assessments, and traditional-use signals. You provide processed CSV evidence to generate scorecards and a ranking summary.

A limitation of multi-criteria scoring for natural product prioritization is its dependence on heterogeneous biomedical evidence availability. Missing evidence is conservatively flagged rather than estimated, which can lower scores for poorly studied phytochemicals despite potential efficacy.