Advancing Molecular Machine (Learned) Representations with
  Stereoelectronics-Infused Molecular Graphs
Authors: name: Daniil A. Boiko, name: Thiago Reschützegger, name: Benjamin Sanchez-Lengeling, name: Samuel M. Blau, name: Gabe Gomes

Molecular representation is a foundational element in our understanding of
the physical world. Its importance ranges from the fundamentals of chemical
reactions to the design of new therapies and materials. Previous molecular
machine learning models have employed strings, fingerprints, global features,
and simple molecular graphs that are inherently information-sparse
representations. However, as the complexity of prediction tasks increases, the
molecular representation needs to encode higher fidelity information. This work
introduces a novel approach to infusing quantum-chemical-rich information into
molecular graphs via stereoelectronic effects. We show that the explicit
addition of stereoelectronic interactions significantly improves the
performance of molecular machine learning models. Furthermore,
stereoelectronics-infused representations can be learned and deployed with a
tailored double graph neural network workflow, enabling its application to any
downstream molecular machine learning task. Finally, we show that the learned
representations allow for facile stereoelectronic evaluation of previously
intractable systems, such as entire proteins, opening new avenues of molecular
design.