Extended Data Fig. 4: Assessment of yield prediction performance by RF model and Chemma on two HTE reactions: Suzuki-Miyaura and Buchwald-Hartwig.
From: Large language models to accelerate organic chemistry synthesis
(a-c) The distribution of the yields for Pd-catalysed Suzuki–Miyaura [HTE], Buchwald-Hartwig [ELN], and imidazole C-H arylation [HTE] reactions. (d, e) Test set performance of the RF model and Chemma with randomly split strategy. A gradual erosion in predictive accuracy occurred from 90% of the entire dataset down to 5% of the full data set. (f, g) Test set performance of the RF and Chemma when training and test sets are split by diverse substrate scopes. For Suzuki-Miyaura reaction, all reactions can be split by twenty different kinds of substrates. For the Buchwald-Hartwig reaction, all reactions are split by fifteen aryl chloride substrates. We define four scenarios for evaluation characterized by variable training and testing substrates. For instance, a scenario encompasses reactions involving eight substrates in the training phase and reactions with four substrates for testing. (h, i) Test set performance of the RF model and Chemma by isolating conditions sets. For the Suzuki-Miyaura reaction, all reactions are split by eleven different kinds of ligands. For the Buchwald-Hartwig reaction, we select four case scenarios that had been tested in for evaluation. Training and testing sets are divided with additives scopes. Accordingly, in Case-1, tested additives are enumerated by indices a10, a18, a15, a23, and a4; in Case-2, indices a11, a9, a1, a17 and a5 are selected for testing; in Case-3, indices a14, a8, a21, a12, a6 are selected for testing; in Case-4, indices a16, a2, a22, a20, a3 are selected for testing. (j-k) The distribution of free energy barriers on two reactions: chiral phosphoric acid-catalyzed thiol addition and radical C-H functionalization reaction. (l) Chemma’s performance on two selective datasets. Each training sets with different proportions are selected randomly from the original full data set, and 30% data of the entire datasets are randomly selected as test sets.
