While there is no general rule determining the minimum ratio between the dimensions of explanatory variables and the number of learning data points, one benchmark suggests that a ratio of over 10:1 is a good starting point. Therefore, having at least a few thousand data points is desirable as a general guideline for progressing molecular informatics. Unfortunately, most experimental informatics projects fail to meet this requirement, illustrating a critical challenge in the field. Several data science methodologies, such as sparse modeling techniques like Lasso and Ridge, feature engineerings techniques like Boruta, and information criterion (e.g., Akaike criterion), have been reported to fit well with databases. These methodologies remove parameters with low relevance to the target variable based on certain statistical rules. Such data science tools are compatible with experimental informatics that struggle with small-scale data, producing notable results. However, these techniques do not essentially transcend the "Ugly Duckling" theorem, often resulting in decreased accuracy with untrained data. These problems arise from data-oriented feature selection methods, overfitting to the training data, or mismatches between the mathematical algorithms used for dimensionality reduction and the behavior of the target material system. According to the "Ugly Duckling" theorem, the only way to successfully apply machine learning to small-scale data sets is to provide some assumptions or prior knowledge of the regression system. This corresponds to the task of pre-extracting parameters correlated with the predicted properties, based on the domain knowledge of the user of the prediction modelin this case, knowledge of chemistry or materials science. Alternatively, recent deep learning approaches have gained attention, such as transfer learning methods that deploy deep learning models trained on specific structure-property correlations to other material systems. The deep learning model to be transferred here is an encoder that automatically converts molecular structures into numerical spaces. Thus, specifying a particular trained model for transfer is essentially the same as humans choosing explanatory variables generated by cheminformatics techniques.