Images of chemical structures as molecular representations for deep learning

Implementing Artificial Intelligence for chemical applications provides a wealth of opportunity for materials discovery, healthcare and smart manufacturing. For such applications to be successful, it is necessary to translate the properties of molecules into a digital format so they can be passed to the algorithms used for smart modelling. The literature has shown a wealth of different strategies for this task, yet there remains a host of limitations. To overcome these challenges, we present two-dimensional images of chemical structures as molecular representations. This methodology was evaluated against other techniques in both classification and regression tasks. Images unlocked (1) superior augmentation strategies, (2) application of specialist network architectures and (3) transfer learning, all contributing to superior performance and without prior specialised knowledge on cheminformatics required. This work takes advantage of image feature maps which do not rely on chemical properties and so can represent multi-component systems without further property calculations. Graphical abstract: [Figure not available: see fulltext.]