ArticleJournal of computational chemistry2021
ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs.
Article in Journal of computational chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Undirected Exploration of Binding Pockets with Flexible Topology.Journal of chemical theory and computation · 2025Article
- AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction.Journal of chemical information and modeling · 2025Article
- Article
- Flexible Topology: A Dynamic Model of a Continuous Chemical Space.Journal of chemical theory and computation · 2023Article
- Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions.Molecules (Basel, Switzerland) · 2023Article
- Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity.ACS omega · 2023Article
- Evaluation of log P, pKJournal of computer-aided molecular design · 2021Article
- Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method.Journal of computer-aided molecular design · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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. 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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Registered trials
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