C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles

Abstract

We report a general C–H aminoalkylation of 5-membered heterocycles through a combined machine learning/experimental workflow. Our work describes previously unknown C–H functionalization reactivity and creates a predictive machine learning (ML) model through iterative refinement over 6 rounds of active learning. The initial model established with 1,3-azoles predicts the reactivities of N-aryl indazoles, 1,2,4-triazolopyrazines, 1,2,3-thiadiazoles, and 1,3,4-oxadiazoles, while other substrate classes (e.g., pyrazoles and 1,2,4-triazoles) are not predicted well. The final model includes the reactivities of additional heterocyclic scaffolds in the training data, which results in high predictive accuracy across all of the tested cores. The high prediction performance is shown both within the training set via cross-validation (CV R² = 0.81) and when predicting unseen substrates of diverse molecular weight and structure (Test R² = 0.95). The concept of feature engineering is discussed, and we benchmark mechanistically related DFT-based features that are more time-intensive and laborious in comparison with molecular descriptors and fingerprints. Importantly, this work establishes novel reactivity for heterocycles for which C–H functionalization methods are underdeveloped. Since such heterocycles are key motifs in drug discovery and development, we expect this work to be of significant use to the synthetic and synthesis-oriented ML communities.