ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs
| Autor: | Alex Dickson, Matthew J. Hirn, Nazanin Donyapour |
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| Rok vydání: | 2021 |
| Předmět: |
Hyperparameter
010304 chemical physics Artificial neural network Field (physics) General Chemistry Molecular Dynamics Simulation 010402 general chemistry 01 natural sciences Force field (chemistry) Article 0104 chemical sciences Computational Mathematics Partial charge Molecular dynamics Generalized forces 0103 physical sciences Range (statistics) Statistical physics Neural Networks Computer Databases Chemical Mathematics |
| Zdroj: | J Comput Chem |
| ISSN: | 1096-987X |
| Popis: | This work examines methods for predicting the partition coefficient (log P) 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 log P predictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the log P values from four independent test sets. The ClassicalGSG method’s performance is compared to a baseline model that employs graph convolutional networks (GCNs). Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized Force Field (CGenFF) and 2D molecular structures. |
| Databáze: | OpenAIRE |
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