In our study, we utilized the Random Forest Regressor (RFR) in the Scikit-learn package for high-throughput screening. To determine the optimal hyperparameters, we employed cross-validation, calculating scores across a predefined grid of hyperparameter spaces. Specifically, the training dataset was divided into ten subsets; nine were used for training the model, while the remaining subset served to evaluate its performance. We chose the mean squared error (MSE) as the metric for optimizing hyperparameters. To prepare our models, we first randomized the dataset, allocating 85% of the data for training and the remaining 15% for validation. The performance of each trained model was evaluated using three metrics: the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R 2 ) calculated from the predictions on the test set as described in SI (Sect. II). To report the performance metrics, we calculated each parameter 20 times and then computed the average value along with the standard deviation.