A Governed Multi-Node Computational Discovery Pipeline: Pre-Specified Gates, Bounded Claims, and Honest Branch Closure in Phytochemical Polypharmacology
Abstract
Abstract Computational multi-target profiling is technically accessible but methodologically ungoverned: thresholds set post-hoc, claims exceeding the data, failed branches omitted, and ΔG values untraceable to source files. We describe a platform that addresses these failure modes by construction. The platform enforces pre-specified gates (frozen in named decision sheets before any simulation runs), outcome-keyed ceiling templates (format_verdict.py, NemoClaw constitutional review), a 46-check regression oracle, and a SHA256 provenance chain linking every published number to the ΔTOTAL line of the source FINAL_RESULTS_DECOMP.dat file. All closed cycles feed a retrieval-first knowledge graph. Applied to nine fig and olive phytochemicals across two targets: (Node 1) nine compounds versus COX-2 (5KIR), N = 4 replicates each, ALL_PASS; oleocanthal ΔG = − 61.8 kcal/mol, CV = 2.4%, ΔΔG = − 22.9 kcal/mol versus celecoxib. (Node 2) luteolin versus IKKβ (4KIK): initial CV = 16.1% (FAIL); structural diagnosis and pre-registered deep-pocket reseed produced ALL_PASS (mean ΔG = − 31.457 kcal/mol, CV = 6.85%, N = 3). (Node 3) quercetin versus IKKβ: generalization, ALL_PASS (mean ΔG = − 24.603 kcal/mol, CV = 3.04%, N = 3). (Node 4) quercetin versus p65 (1VKX): POSE_UNSTABLE, branch closed; platform claim bounded to two-node model. All results are EXPLORATORY DDT tier: computationally reproducible under the stated protocol, not biologically validated. Appropriate next steps are enzymatic…
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