DeepCocrystal represents molecules as SMILES strings . SMILES are obtained by traversing a molecular graph starting from a non-hydrogen atom, and by annotating atoms and bonds with specific symbols (Fig. ). Different SMILES strings for the same molecule can be obtained by starting from a different non-hydrogen atom and/or following a different path for graph traversal (Fig. ). DeepCocrystal leverages convolutional Neural Networks (CNNs) for 'chemical language' processing. CNNs are a class of deep learning models commonly used for processing sequences of text . Via convolution -which involves sliding a filter (kernel) over the input text -CNNs can capture information and features at different levels of abstraction, and progressively aggregate it to provide a prediction. CNNs have been previously applied to predict the properties of single molecules from their SMILES strings , and have shown optimal performance compared to other SMILESprocessing architectures .