AutoGraph: Autonomous Graph-Based Clustering of Small-Molecule Conformations

Document Type

Article

Publication Date

3-29-2021

Publication Title

Journal of Chemical Information and Modeling

Abstract

While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble appears as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-l-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provides partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess and check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model’s loss function.

Volume

64

Issue

4

First Page

1647

Last Page

1656

DOI

10.1021/acs.jcim.0c01492

ISSN

1549-9596

Rights

Copyright © 2021 American Chemical Society

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