A research pathway for Bayesian-optimization virtual screening: upload your library (surrogate score, uncertainty, oracle scores), and it drives one step of the drift-aware campaign - Expected-Improvement next-batch selection (downloadable), an escalation recommendation (acquire vs escalate oracle fidelity) via a closure ladder, plus retrospective correlation / top-1% overlap / retrieval / enrichment.
A research pathway for Bayesian-optimization virtual screening: upload your library (surrogate score, uncertainty, oracle scores), and it drives one step of the drift-aware campaign - Expected-Improvement next-batch selection (downloadable), an escalation recommendation (acquire vs escalate oracle fidelity) via a closure ladder, plus retrospective correlation / top-1% overlap / retrieval / enrichment. Deterministic; the ML surrogate is your input, not trained here.
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Open it inside GDBS to save runs to Sandbox, attach results to a Worklog case, or share through a Gate client portal - all in the browser, nothing uploaded to anyone’s cloud.