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

Kiyoto Aramis Tanemura, Susanta Das, Kenneth M. Merz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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. Associated codes are available at

Original languageEnglish
Pages (from-to)1647-1656
Number of pages10
JournalJournal of Chemical Information and Modeling
Issue number4
StatePublished - 26 Apr 2021
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank the high-performance computing center (HPCC) at Michigan State University for providing their computational resources. The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: NIH (grant 1U2CES030167-01).

Publisher Copyright:
© 2021 American Chemical Society.


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