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This article is categorized under: Data Science > Chemoinformatics Understanding how to best represent molecules in a machine‐readable format is a key challenge. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. 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. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Three of the most significant representations are simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. Four classes of representations are introduced: string, connection table, feature‐based, and computer‐learned representations. 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. Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Lab notebook entries must target both visualisation by scientists and use by machine learning algorithms We discuss some of the complex issues involved in fixing current methods, as well as some of the immediate benefits that can be gained when open data is published correctly using unambiguous machine readable formats. Making this goal practically achievable will require a paradigm shift in the way individual scientists translate their data into digital form, since most contemporary methods of data entry are designed for presentation to humans rather than consumption by machine learning algorithms. We argue that the most significant immediate benefactor of open data is in fact chemical algorithms, which are capable of absorbing vast quantities of data, and using it to present concise insights to working chemists, on a scale that could not be achieved by traditional publication methods. We propose that this trend be accompanied by a thorough examination of data sharing priorities. sy2), Tripos Sybyl Line Notation (.sln), Beilstein ROSDAL (.ros), XYZ Files (.The current rise in the use of open lab notebook techniques means that there are an increasing number of scientists who make chemical information freely and openly available to the entire community as a series of micropublications that are released shortly after the conclusion of each experiment. mmod), Schrödinger Maestro (.mae), Standard Molecular Data (.smd), Tripos Mol2 (.mol2. ent), RCSB Protein Data Bank Markup Language (.xml. mmcif), RCSB MacroMolecular Transmission Format (.mmtf), RCSB Protein Data Bank Files (.pdb. rd), MDL RXNFiles, both V2000 and V3000 connection tables (.rxn), MMI SketchEl Molecule (.el), Molinspiration JME String (.jme), RCSB Binar圜IF (.bcif), RCSB Macromolecular Crystallographic Information File (.cif. dx), ISIS Sketch File (.skc), ISIS Sketch Transportable Graphics File (.tgf), MDL MOLFiles, both V2000 and V3000 connection tables (.mol. smiles), IUPAC InChI (.inchi), IUPAC JCAMP-DX (.jdx. Read and write many popular chemical file types for working with the applications you use:ĪCD/ChemSketch Documents (.sk2), ChemDoodle Documents (.icl), ChemDoodle 3D Scenes (.ic3), ChemDoodle Javascript Data (.cwc.js), CambridgeSoft ChemDraw Exchange (.cdx), CambridgeSoft ChemDraw XML (.cdxml), Crystallographic Information Format (.cif), CHARMM CARD File (.crd), ChemAxon Marvin Document (.mrv), Chemical Markup Language (.cml), Daylight SMILES (.smi.