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, without the need for additional explicit chemical information, or the “grammar” of how SMILES encode structural data. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for learning a range of distinct chemical properties including toxicity, activity, solubility and solvation energy, while outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, this localization identifies specific parts of a chemical that is consistent with established first-principles knowledge of solubility with an accuracy of 88%, demonstrating that neural networks can learn technically accurate chemical concepts. The fact that SMILES2vec validates established chemical facts, while providing state-of-the-art accuracy, makes it a potential tool for widespread adoption of interpretable deep learning by the chemistry community.