Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Inspired by natural language processing, techniques, we here introduce Mol2vec, which is an unsupervised machine learning,approach to, learn vector representations: of molecular substructures. Like the Word2vec models, wherevectors of closely related words are in dose proximity in the vector space, Mol2vec learns Vector representations of molecular,,Substructures that point in similar directions for chemically, related substructures. compounds can finally be encoded as vectors by summing the vectors of the individual substructures and, for instance, be fed into supervised machine learning approaches to predict compound properties:. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning, approach on a so-Called Corpus of compounds that consists of all available chemical-matter. The resulting Mol2vec model is pretrained once, yields dense vector,representations, and overcomes drawbacks of common, compound feature, representations such:as sparseness and bit collisions: The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results-obtained for Morgan fingerprints as a reference Compound representation: Mol2vec can-be-easily combined with ProtVec, Which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment-independent and thus can also be easily used for proteins with low sequence similarities.