P57 - Partial Charge Prediction and Pattern Extraction from a AttentiveFP Graph Neural Network
DescriptionMolecular dynamics (MD) simulations enable the time-resolved study of bio-molecular processes. The quality of MD simulations is, however, highly dependent on the set of interaction parameters used, so-called force fields. The accurate partial-charge assignment of all simulated atoms is hence a crucial part of every MD simulation. Due to the slowly decaying nature of the Coulomb interactions, the effects of different partial-charge assignments can be observed over long distances and can have drastic effects on the stability of a MD simulation. Therefore, many schemes have been developed over the last decades to improve partial-charge assignment: Classical tabulated values, ab initio calculations, or the prediction with a machine learning model. However, all these approaches have some shortcomings in either accuracy, speed, or interpretability. Here, we present an option to combine the accuracy of ab initio calculations, the speed of machine learning models, and the interpretability of tabulated assignments. An attention-based graph neural network is trained on a diverse dataset to predict high-quality atom-in-molecule (AIM) partial charges. We then use a model-agnostic approach to extract the most important sub-graph on an atomistic level to provide the user with the same level of interpretability as for tabulated values.
TimeTuesday, June 2719:30 - 21:30 CEST