By utilizing SOAP features, the XGB classifier correctly predicted the most active site in 28 out of 30 molecules, achieving an impressive accuracy of 93% (Fig. ). This high performance is particularly notable given the small dataset of 124 atomic sites and 30 molecules. One incorrectly predicted molecule was 2-(methylsulfonyl)pyrimidine, highlighting a limitation of our dataset -this sulfur-containing molecule was unique, leaving the classifier with no other sulfur-containing examples to learn from. In addition to XGB, other classification models, including random forest, logistic regression, neural networks, and Gaussian process, were tested, achieving comparable or lower performance (Table .8.1).