Cost-Aware Multi-Fidelity Bayesian Optimization with Runtime-Gated Cascading Tiers for Autonomous in silico Dermatology Discovery

Autonomous virtual-screening pipelines suffer a recurring failure mode: cheap predictors (Boltz-2 affinity, ADMET, xTB conformer scoring) accumulate thousands of candidate scores, yet expensive validation tiers (long molecular dynamics, absolute binding free energy, wet-lab) advance unevenly because the scheduler cannot reason simultaneously about cost, information value, gate-policy, and runtime availability of each tier. We describe a cost-aware multi-fidelity Bayesian optimization (BO) scheduler that operates over a unified evidence ledger (789 compound–target rows, 136 columns) and a 7-tier cascade ranging from Boltz-2 cofold (~minutes/pair, GPU) through OpenFold3 + AQAffinity (~minutes/pair, GPU) to wet-lab IC₅₀ / IVRT / IVPT (~₩100k+/sample, CRO). The acquisition score blends expected-improvement on missing axes against a tier-specific cost prior; scientific gates (scientificgates. yaml) and curator directives veto advance for compounds failing safety, novelty, or applicability-domain criteria. To prevent the scheduler from pushing work onto a tier whose runtime is missing, we add a runtime probe that resolves each tier's executable, model checkpoint, and dependency before scoring; tiers with missing runtime are flagged as runtimeblocked rather than gate-blocked, and stalled work-items (consecutiveᵣuns ≥ 3 in identical signature) are recorded in a per-run queue-state JSON for downstream observability. We report the scheduler's behavior on a current dermatology workload: 752 compound–target pairs queued for the OpenFold3 advance, top acquisition score 0. 937, distributed across 8 protein targets (TYR, DCT, MMP1, TYRP1, LOX, CTGF, TGFB1, PTGS2). The runtime-gate distinction proved decisive: when AQAffinity calibration was incomplete, 124 work-items were correctly held under mmp1ᵦincₚending rather than spuriously advanced. We discuss the design as a reusable substrate for in silico dermatology pipelines, the gap between cheap-tier saturation and expensive-tier throughput, and the limitations of cost-prior elicitation when tier-specific runtimes evolve. Keywords: multi-fidelity Bayesian optimization, cost-aware acquisition, autonomous discovery, evidence ledger, scientific gates, runtime gating, dermatology, OpenFold3, AQAffinity, Boltz-2 ---