To verify the correlation between the Autodock binding affinities and their bioactivity, we performed a dimension reduction procedure using unsupervised learning on the CF dataset, which consists of molecular structures with binding affinity data (see Figure ). We used unsupervised learning here to make the point that unsupervised learning underperforms when only structural data is provided. Specifically, if the classification accuracy is improved with additional feature inputs, those features must contain some information to discriminate among the population. In other words, if the inclusion of binding affinity data enhances the clustering accuracy, it provides another co-descriptor for bioactivity. Indeed, Figures and show that descriptors consisting of chemical structures and binding affinity data give a better separation/distinction between active and inactive molecules compared to the unsupervised learning results based only on chemical structures.