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Title: ClassicalGSG : Prediction of log P using classical molecular force fields and geometric scattering for graphs
Abstract

This work examines methods for predicting the partition coefficient (logP) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index‐invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach “ClassicalGSG” and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG logPpredictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the logPvalues from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures.

 
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Award ID(s):
1845856 1761320
PAR ID:
10452728
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Journal of Computational Chemistry
Volume:
42
Issue:
14
ISSN:
0192-8651
Page Range / eLocation ID:
p. 1006-1017
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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