DS, ML, and data-driven science have emerged as the 4 th paradigm of science . Hence, DS and ML have gained importance in chemistry and correlated disciplines such as materials science , biology , and health sciences . Despite the growing interest and research in ML tools for chemistry, data collection and storage are critical steps in ML model designing and validation because the dataset size is considerably important in ML. ML often requires large datasets for training, and insufficient data may cause overfitting or underfitting problems, which leads to poor predictive models. Nevertheless, large chemical datasets are becoming widely available , and access to data for ML design will not be a threat as the importance of open science and FAIR principles increases.