A5092031247- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Protein Structure and Dynamics
- Crystallography and molecular interactions
- Advanced Graph Neural Networks
- Carbon dioxide utilization in catalysis
Roche (Switzerland)
2024
ETH Zurich
2023-2024
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on hierarchical tree constructed from attention values extracted graph neural network (GNN), which was trained to predict atomic accurate quantum-mechanical (QM) calculations. resulting dynamic attention-based substructure hierarchy (DASH) provides fast assignment with the same accuracy as GNN itself, software-independent, can easily be integrated existing...
Recently, we presented a method to assign atomic partial charges based on the DASH (dynamic attention-based substructure hierarchy) tree with high efficiency and quantum mechanical (QM)-like accuracy. In addition, approach can be considered “rule based”—where rules are derived from attention values of graph neural network—and thus, each assignment is fully explainable by visualizing underlying molecular substructures. this work, demonstrate that these hierarchically sorted substructures...
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on hierarchical tree constructed from attention values extracted graph neural network (GNN), which was trained to predict atomic accurate quantum-mechanical (QM) calculations. resulting dynamic attention-based substructure hierarchy (DASH) provides fast assignment with the same accuracy as GNN itself, software-independent, can easily be integrated existing...
Recently, we presented a method to assign atomic partial charges based on the DASH tree (dynamic attention-based substructure hierarchy) with high efficiency and quantum mechanical (QM) like accuracy. Additionally, approach can be considered “rule based” – where rules are derived from attention values of graph neural network thus, each assignment is fully explainable by visualizing underlying molecular substructures. In this work, demonstrate that these hierarchically sorted substructures...