Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs

Understand

Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning.

• 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, enhancing expressivity and interpretability.