A review of molecular representation in the age of machine learning

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Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine-readable format, as this is central to computational chemistry. Four classes of representations are introduced: string-, connection table-, feature based-, and computer learned-representations. Three of the most significant representations are SMILES, InChI, and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein we present questions for consideration in future work which we believe will make chemical variational autoencoders even more accessible.

chemoinformatics, fingerprints, machine learning, molecular representation, variational autoencoder
Journal Title
Wiley Interdisciplinary Reviews: Computational Molecular Science
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Engineering and Physical Sciences Research Council (EP/S024220/1)
Engineering and Physical Sciences Research Council (2276995)