We manually collected a Young's modulus data from the published papers. At least, molecular information represented as simplified molecular input line entry system (SMILES) and the reported Young's modulus are registered in this dataset. SMILES was then encoded as numerical representation of the number of specific functional groups using RDKit library. Each molecule was converted into an 85-dimensional vector with non-negative integers. After removing variables with high correlation among explanatory variables, we performed LASSO regression implemented by scikit-learn library. In the model training, ! "# ∑(𝑦 -𝑦 %) ! + 𝛼|𝑤| was used as the loss function. Here, 𝑛 is the number of samples, 𝑦 represents the actual measured value, 𝑦 % is the predicted value, 𝑤 represents the coefficients of the explanatory variables, and 𝛼 is a hyperparameter. The 𝛼 was optimized to minimize ! # ∑(𝑦 -𝑦 %) ! × (𝑁 "#$ -8) ! , where 𝑁 "#$ is the number of non-zero coefficients. This metrics is the product of the mean square error and the constraint for variable selection. The dataset was split into training and test sets in an 8:2 ratio. The hyperparameters were tuned using 5-fold crossvalidation on the training set.