Discussing the issue more quantitatively, the dilemma arises from an imbalance between the dimensions of the explanatory variables and the number of data points available for learning. Over years of cheminformatics research, numerous methods have been proposed to describe the characteristics of molecular structures numerically. Typical molecular descriptors have dimensions in the hundreds, and fingerprints can possess bits in the thousands. Recent advancements in deep learning for molecular recognition often involve latent vectors of several hundred dimensions. Consequently, a vector of several hundred dimensions is generally required to characterize a molecular structure.