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Ferrocene derivatives have a wide range of applications, including as ligands in asymmetric catalysis, due to their chemical stability, rigid backbone, steric bulk, and ability to encode stereochemical information via planar chirality. Unfortunately, few of the available molecular mechanics force fields incorporate parameters for the accurate study of this important building block. Here, we present a MM3* force field for ferrocenyl ligands, which was generated using the quantum-guided molecular mechanics (Q2MM) method. Detailed validation by comparison to DFT calculations and crystal structures demonstrates the accuracy of the parameters and uncovers the physical origin of deviations through excess energy analysis. Combining the ferrocene force field with a force field for Pd–allyl complexes and comparing the crystal structures shows the compatibility with previously developed MM3* force fields. Finally, the ferrocene force field was combined with a previously published transition-state force field to predict the stereochemical outcomes of the aminations of Pd–allyl complexes with different amines and different chiral ferrocenyl ligands, with an R2 of ∼0.91 over 10 examples. 

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki–Miyaura and Buchwald–Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions. 

Abstract The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome.