Advancing Molecular Machine (Learned) Representations with
  Stereoelectronics-Infused Molecular Graphs
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.
66b5f11f18d95c926ce1ca64
Daniil A. Boiko
admin_assigned
2024-08-09T13:31:05.611Z
63e5d9fb7f9730f52364fe56
/avatars/8d4d5df17c6c23f3c11f03e471ba896f.svg
Daniil Boiko
user
dboiko
66b5f11f18d95c926ce1ca65
Thiago Reschützegger
admin_assigned
2024-08-09T13:31:12.501Z
65a97b6360170e8025573ade
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/WJvyvILyJ-bT39nY9jdd-.jpeg
Thiago Reschützegger
user
thiagoreschutz
66b5f11f18d95c926ce1ca66
Benjamin Sanchez-Lengeling
66b5f11f18d95c926ce1ca67
Samuel M. Blau
66b5f11f18d95c926ce1ca68
Gabe Gomes
admin_assigned
2024-08-09T13:31:32.674Z
6412922a227028b63e327a37
/avatars/b1fa6c60472f97a191160bb15bcef962.svg
Gabe Gomes
user
gabegomes
Advancing Molecular Machine (Learned) Representations with
  Stereoelectronics-Infused Molecular Graphs
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.
Enhancing molecular machine learning models with stereoelectronic effects through a double graph neural network improves performance and enables evaluation of complex systems like proteins.
molecular machine learning models molecular graphs stereoelectronic effects double graph neural network stereoelectronic evaluation