The prediction of the biological and physicochemical properties of molecules are the most common applications of ML and DS in chemistry because they aid drug and material discoveries. When applied to predicting biological and physical-chemical properties, ML and DS tools are often referred to as quantitative structure--active relationship (QSAR) when predicting biological properties and quantitative structure--property relationship (QSPR) when predicting nonbiological properties. QSAR and QSPR are widespread in chemistry, and publications related to these themes often range from simple statistical regression and classification models to sophisticated support vector machines (SVM) , artificial neural networks (ANN) , and ML ensembles . Although chemometric and statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and partial least squares (PLS) have been methods of choice for years in QSAR and QSPR studies, ML-based models are now preferred over those classical methods, as the diversity and availability of chemical data increases. For example, pKa, which has been modeled by MLR, PCA, and PLS for years, has been successfully modeled by Mansouri et al. using multiple ML approaches like an ensemble of SVM combined with k-nearest neighbors (kNN), extreme gradient boosting (XGB) and deep neural networks. These models were built using publicly available data; the source code and data are provided on GitHub, and the performance of these models is compared favorably to the commercial products. Other examples of QSAR/QSPR modeling using ML include the discovery of new materials , the prediction of ionic liquid properties , the modeling of drugs , the quality control of fuels , and the prediction and modeling of bioconcentration and toxicity of organic compounds . Hence, ML is a valuable tool for QSAR/QSPR to provide confident results and outperform chemometric methods.