Stereoelectronics-Aware Molecular Representation Learning

19 July 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The representation of molecular structures is crucial for molecular machine learning strategies. Although graph representations are highly versatile and show their broad applicability, they lack information about the quantum-chemical properties of molecular structures. This work proposes a new way to infuse such information into molecular graphs, using a supervised learning method. As a result, the model is able to predict essential higher-order interactions between electron-rich and electron-deficient localized orbitals. The learned interactions are then used as a representation for the prediction of downstream tasks, improving over QM9 baselines.


machine learning
computational chemistry


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