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AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations

submitted on 28.12.2020, 18:35 and posted on 29.12.2020, 12:03 by Kiyoto Tanemura, Susanta Das, Kenneth M. Merz Jr.

While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems 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 provide 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. Associated codes are available at .


NIH grant 1U2CES030167-01


Email Address of Submitting Author


Michigan State University


United States of America

ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no conflict of interest