Coordinate-Free and Low-Order Scaling Machine Learning Model for Atomic Partial Charge Prediction for Any Size of Molecules
Author:
Affiliation:
1. Department of Materials, Imperial College London, SW7 2AZ London, U.K.
Funder
Thomas Young Centre
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00376
Reference23 articles.
1. DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges
2. NNAIMQ: A neural network model for predicting QTAIM charges
3. A model of atoms in molecules based on potential acting on one electron in a molecule: I. Partition and atomic charges obtained from ab initio calculations
4. Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models
5. Advancements in small molecule drug design: A structural perspective
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