  Comprehensive and unambiguous identification of small molecules in complex
samples will revolutionize our understanding of the role of metabolites in
biological systems. Existing and emerging technologies have enabled measurement
of chemical properties of molecules in complex mixtures and, in concert, are
sensitive enough to resolve even stereoisomers. Despite these experimental
advances, small molecule identification is inhibited by (i) chemical reference
libraries representing <1% of known molecules, limiting the number of possible
identifications, and (ii) the lack of a method to generate candidate matches
directly from experimental features (i.e. without a library). To this end, we
developed a variational autoencoder (VAE) to learn a continuous numerical, or
latent, representation of molecular structure to expand reference libraries for
small molecule identification. We extended the VAE to include a chemical
property decoder, trained as a multitask network, in order to shape the latent
representation such that it assembles according to desired chemical properties.
The approach is unique in its application to small molecule identification,
with its focus on m/z and CCS, paired with its training paradigm, which
involved a cascade of transfer learning iterations. This allows the network to
learn as much as possible at each stage, enabling success with progressively
smaller datasets without overfitting. Once trained, the network can rapidly
predict chemical properties directly from structure, as well as generate
candidate structures with desired chemical properties. Additionally, the
ability to generate novel molecules along manifolds, defined by chemical
property analogues, positions DarkChem as highly useful in a number of
application areas, including metabolomics and small molecule identification,
drug discovery and design, chemical forensics, and beyond.
