Benchmark choice decides which Bayesian model ranks reaction conditions best: 49.3% vs 41.9%
bravo_abad · x · 2026-09-27
Endo and Kaneko (JCIM 2026) tested two Gaussian-process Bayesian optimization models for chemistry—yield prediction vs. pairwise ranking, both with Thompson sampling. With a shared large search space across molecule pairs, yield prediction ranks candidates better; with per-reaction searches, ranking wins: after 10 rounds it finds 49.3% of top-1%-yield conditions vs. 41.9% for yield prediction, averaged over 12 reactions. The authors caution the setup also changes exploration coverage, but the takeaway stands: a model that looks stronger on broad collections may not help your bench reaction—decide which experimental choice the model must support before picking one.
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