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