Smiles2vec: Predicting Chemical Properties from Text Representations
نویسندگان
چکیده
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES strings to predict a broad range of chemical properties, including toxicity, activity, solubility and solvation energy. Furthermore, we trained an interpretability mask for SMILES2vec solubility prediction, which identifies specific parts of a chemical that is consistent with groundtruth knowledge with an accuracy of 88%, demonstrating that neural networks can learn technically accurate chemical concepts.
منابع مشابه
SMILES2vec
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES strings to predict chemical properties, wi...
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