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

Tanemura, Kiyoto Aramis; Das, Susanta; Merz, Kenneth M.

doi:10.1021/acs.jcim.0c01492

Cited by 18 publications

(17 citation statements)

References 49 publications

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“…The solution to this dissimilarity-set problem is useful in chemistry and biology, for instance, for finding the most geometrically dissimilar sets of conformers (or molecular structures) to efficiently span conformational space and eliminate redundant structures. The use of root-mean-square deviation (RMSD) of atomic positions for selecting sets of conformers has been used by many groups [ 2 , 5 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

et al. 2023

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Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

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Section: Introductionmentioning

confidence: 99%

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

et al. 2023

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“…Initially, we determine the molecular protonation state of the corresponding gas-phase ions (typically [M + H] + or [M – H] − ) and then generate the conformations for these using the RDKit toolkit. − All generated conformations undergo geometry optimization using a QM-based ML model called ASE_ANI − followed by an unsupervised clustering step using in-house unsupervised clustering code ( viz . AutoGraph) to obtain structurally distinct conformations. Standard DFT geometry optimization and atomic charge calculation are then performed on a representative conformation from each cluster at the B3LYP/6-31+G(d,p) and B3LYP/6-311++G(d,p) level of theory, respectively, using the Gaussian 16 software package. − The input file for CCS calculation is prepared by extracting the geometry and atomic charges from the DFT computations.…”

Section: Methodsmentioning

confidence: 99%

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

Das

Tanemura

Dinpazhoh

et al. 2022

J. Am. Soc. Mass Spectrom.

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The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

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“…Graph-based conformer clustering methods, such as AutoGraph, are helpful for generating ensembles of lowest-energies conformers after conformers have been processed with semi-empirical methods [151].…”

Section: Handling Isomerismmentioning

confidence: 99%

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

Sharma

Arya

Cruz

et al. 2021

Life

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Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

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

Cited by 18 publications

References 49 publications

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

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