Much like the NIST dataset, the Chemotion dataset also exhibits class imbalance, the overall distribution of functional groups partially mirrors that of the NIST. Distribution plots provide insights into the class distribution present in the dataset which helps to analyze data-driven models. Figure subset examined in this study resulted from a subset with molecules containing nitriles or alkyl halides. This subset contains 571 samples. It is important to note that this designation does not imply exclusive compositions of nitriles and alkyl halides in the samples but rather signifies the presence of at least one of these functional groups alongside others. In simple terms, it means that the distribution of the sliced dataset facilitates better generalization, and not the presence of nitriles and alkyl halides in the samples. We show this in the results section. To further analyse the similarities between the NIST and Chemotion datasets, we employ a principal component analysis (PCA) technique to the Morgan fingerprints extracted from the NIST dataset. We then utilize this fitted PCA model to transform and visualize the Morgan fingerprints of the Chemotion dataset. This process involves capturing the underlying structure and patterns inherent in the NIST dataset through PCA transformation. By extracting principal components that encapsulate the maximum variance within the NIST data, the PCA model effectively reduces the dimensionality of the dataset while retaining crucial information. This enables a comparative analysis between the datasets within a lower-dimensional space, offering insights into their similarities and differences. Figure shows the scatter plot of the datasets projected onto the principal components of the NIST dataset. It can be observed from the scatter plot of the Chemotion dataset that data points are projected onto a similar space as that of NIST. The density of the Chemotion dataset is close to one of the clusters of the NIST dataset with very few data points in another cluster.