The final remarkable weakness of ML is that if the data have many errors, the ML model will not be reliable . Hence, the accuracy of ML models depends not only on the algorithm but also on the quality of the dataset and the ability of the data scientist to choose and tune the suitable model. Despite some criticism, ML is increasingly used in chemistry. This field is rapidly adopting stateof-the-art ML algorithms and tools such as deep learning , tensors , and transformers to solve and model chemical problems such as drug and polymer design, QSAR and QSPR studies on big data and huge datasets , and even to boost ab initio calculations . As ML applications in chemistry are broad, open-source, and free frameworks and tools to assist chemists in developing ML models, such as OpenChem and ML4Chem , have been designed. These frameworks encapsulate other frameworks, such as PyTorch and scikit-learn, making training and testing ML models in chemistry more straightforward.