On the use of real-world datasets for reaction yield prediction

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.

A Quantum-Guided Molecular Mechanics Force Field for the Ferrocene Scaffold

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.

Stereoselectivity Predictions for the Pd-Catalyzed 1,4-Conjugate Addition Using Quantum-Guided Molecular Mechanics

The conjugate addition of aryl boronic acids to enones is a powerful synthetic tool to introduce quaternary chiral centers, but the experimentally observed stereoselectivities vary widely, and the identification of suitable substrate-ligand combinations requires significant effort. We describe the development and application of a transition-state force field (TSFF) by the quantum-guided molecular mechanics (Q2MM) method that is validated using an automated screen of 9 ligands, 38 aryl boronic acids, and 22 enones, leading to a MUE of 1.8 kJ/mol and a R² value of 0.877 over 82 examples. A detailed error analysis identified the structural origin for the deviations in the small group of outliers. The TSFF was then used to predict the stereoselectivity for 27 ligands and 59 enones. The vast majority of the virtual screening results are in line with the expected results. Selected results for 6-substituted pyrox ligands, which were not part of the training set, were followed up by density functional theory and experimental studies.

Application of Q2MM to predictions in stereoselective synthesis

Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.