Mol2vec is a Python library that allows learning vector representations of molecules using machine learning. Similar to the Word2vec models used in natural language processing, Mol2vec converts molecules into ECFP representation and then treats substructures as words in sentences. Using an unsupervised approach, Mol2vec creates the feature vectors. We used a pretrained model on 20 million compounds provided by the library. The pretrained model converts the given molecules into a 300-dimensional feature vector.