As an example, the validation of the ML model built for BACE1 protein (Uniprot ID: P56817) evaluated with the precision metric is shown in Figure . 2902 active and 1690 inactive compounds were used to build ~3000 models for each run. We use 80% of the total molecules for model construction and fitting, while the remaining 20% constituted the test set. The data partitioning was conducted using a random selection method with a fixed seed, thereby ensuring the reproducibility of these results in subsequent studies. This approach guarantees that the same data split will be replicated consistently, facilitating the validation and comparison of models in future research endeavors. The models were optimized using Optuna and to select the best model we used the Pareto optimization approach , that allow us to minimize the discrepancy between training and validation results, and maximize performance based on the precision evaluation metric. To illustrate the diagnostic ability of the ML BACE1 model evaluated with the precision metric, we plotted sensitivity against the specificity using receiver operating characteristic (ROC) curve, and then calculated the area under the curve (AUC -values between 0 and 1, the higher the better) for both the train and test sets (Figure ). AUC describes the probability of the ML model to rank a randomly chosen positive instance higher than a negative one. In addition, to know whether the model is reliable or not, the calibration curve (or reliability diagram) was plotted (Figure ). This curve compares how well the probabilistic predictions of our binary ML model are calibrated for both test and train sets. Calibration curves that are closer to the diagonal indicate that the ML model is well calibrated. Afterwards, to test the BACE1-ML model we used the 20% of the remaining BACE1 active/inactive molecules and calculated the confusion matrix. This predictive analysis tool provides a structure comparison between true values with model predictive values, and it can be employed to evaluate different metrics used in the models. Within the matrix, four key variables characterize the classification process: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) (Figure ). TP is when the positive data is classified as positive. TN is when the negative data is classified as negative. FP is when the negative data is classified as positive, and FN is when the positive data is classified as negative.