A review of molecular representation in the age of machine learning

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‐re...

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Vydané v:Wiley interdisciplinary reviews. Computational molecular science Ročník 12; číslo 5; s. e1603 - n/a
Hlavní autori: Wigh, Daniel S., Goodman, Jonathan M., Lapkin, Alexei A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA Wiley Periodicals, Inc 01.09.2022
Wiley Subscription Services, Inc
Predmet:
Accessibility
Artificial intelligence
chemoinformatics
Computation
Computational chemistry
Computer applications
fingerprints
Learning algorithms
Machine learning
molecular representation
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Representations
variational autoencoder
ISSN:1759-0876, 1759-0884
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Shrnutí: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 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. 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 VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatics Understanding how to best represent molecules in a machine‐readable format is a key challenge.
Bibliografia:Funding information
Engineering and Physical Sciences Research Council, Grant/Award Number: EP/S024220/1; UCB
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ISSN:1759-0876
1759-0884
DOI:10.1002/wcms.1603