A review of molecular representation in the age of machine learning

Daniel Stauso Wigh, Jonathan M Goodman, Alexei Lapkin

Abstract:

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 machinereadable
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.