We used the random forest regression (RFR) machine learning model as implemented in the Scikit-learn package of Python . The optimal set of hyperparameters was computed using a cross-validation score over a grid of predefined space, where the training data was split into ten groups. The following are the ptimized parameters: "max_depth": 17, "max_features": 0.67, "min_samples_leaf": 1, "min_samples_split": 2, and "n_estimators": 50. To generate diazo compounds molecule geometries, each backbone is systematically designed by two functional groups using the Maestro modeling interface of Schrödinger material science suite (SMSS) . We provide open access to full source code, input files, and data sets at Zenodo's DOI.