  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 to predict chemical properties,
without the need for additional explicit feature engineering. 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
predicting distinct chemical properties including toxicity, activity,
solubility and solvation energy, while also 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, it identified specific parts of a chemical
that is consistent with established first-principles knowledge with an accuracy
of 88%. Our work demonstrates that neural networks can learn technically
accurate chemical concept and provide state-of-the-art accuracy, making
interpretable deep neural networks a useful tool of relevance to the chemical
industry.
