AQME: Automated Quantum Mechanical Environments for Researchers and Educators

Alegre‐Requena, Juan V.; Sowndarya, S. V. Shree; Alturaifi, Turki M.; Pérez‐Soto, Raúl; Paton, Robert S.

doi:10.26434/chemrxiv-2022-dnc48

Cited by 3 publications

(6 citation statements)

References 37 publications

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“…Additionally, common templates for organometallic compounds, such as linear, trigonal planar, and square-planar geometries, can be used. These geometries are not usually obtained with standard RDKit protocols (i.e., square-planar metal complexes lead to tetrahedral structures), which may then neglect an essential aspect of conformational behavior (see the Highlighting the Importance of Specifying the Metal Type: ABEVUZ as an Example section in the ESI 41 ).…”

Section: Conformer Generation and Geometry Refinement: The Csearch An...mentioning

confidence: 99%

“…We conducted a study of 2709 calculations with organic molecules to find the optimal number of QCORR cycles for ground state optimization and frequency calculations in Gaussian 16 (QCORR_benchmarking.zip file in FigShare 41 ). The initial geometries were obtained with a CSEARCH-RDKit standard conformer sampling.…”

Section: Postprocessing Of Output Files: the Qcorr Modulementioning

confidence: 99%

“…It starts with an energy window to remove high energy conformers ( ewin_csearch option, default 5 kcal/mol above lowest), then removes conformers with similar energies ( initial_energy_threshold option, default 0.0001 kcal/mol), and finally removes conformers with similar energy and RMSD ( energy_threshold , default 0.25 kcal/mol and rms_threshold , default 0.25). The default values were chosen based on a benchmark study of flexible druglike compounds and natural products to yield significant conformers while reducing duplicates (see the Benchmarking of CSEARCH‐RDKit and CMIN section in the AQME_ESI.docx document available in FigShare 41 ).…”

Section: Modules Of Aqmementioning

confidence: 99%

“…In this section, we detail three illustrative workflows that have been adapted for different applications regularly carried out in our group, such as calculating energy profiles and generating molecular descriptors for ML models. These workflows are available on Figshare 41 along with associated data and metadata, and three formats are available in the code and the Read the Docs webpage: Jupyter Notebook, SLURM script, and command‐line script. Hands‐on tutorials have been uploaded to YouTube 29 .…”

Section: End‐to‐end Workflowsmentioning

confidence: 99%

“…The modular design of AQME provides opportunities for workflow customization, use in Jupyter notebooks, and integration into other Python projects. 27 Ready-to-use examples with different degrees of complexity are provided via GitHub, 28 supplemented by hands-on video tutorials. 29 These examples can be trivially modified to create workflows that implement different software and levels of theory, or for application to different prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

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AQME: Automated quantum mechanical environments for researchers and educators

Alegre‐Requena

Pérez‐Soto

et al. 2023

WIREs Comput Mol Sci

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AQME, automated quantum mechanical environments, is a free and open‐source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi‐empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble‐averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre‐configured workflows are available in various formats, and hands‐on video tutorials illustrate their use.This article is categorized under: Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming Software > Quantum Chemistry

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Section: Conformer Generation and Geometry Refinement: The Csearch An...mentioning

confidence: 99%

Section: Postprocessing Of Output Files: the Qcorr Modulementioning

confidence: 99%

Section: Modules Of Aqmementioning

confidence: 99%

Section: End‐to‐end Workflowsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

AQME: Automated quantum mechanical environments for researchers and educators

Alegre‐Requena

Pérez‐Soto

et al. 2023

WIREs Comput Mol Sci

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Experimental and computational studies of the production of 1,3-butadiene from 2,3-butanediol using SiO2-supported H3PO4 derivatives

Alegre‐Requena

Hafenstine

Huo

et al. 2023

Chemical Engineering Journal

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Cu-releasing nanoparticles induce the catalytic transamination of amino acids and GSSG under tumor microenvironment conditions

Bonet-Aletá

Alegre‐Requena

Martin-Martin

et al. 2023

Preprint

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Catalytic cancer therapy is emerging as a powerful tool to target cancer cells by exploiting specific characteristics of the tumor microenvironment (TME). To this end, the catalytic activity of nanoparticles, enzymes and homogeneous catalysts is recruited to induce reactions that are damaging to cancer cells. Thus, the pro-drug activation approach uses chemical constructs that become toxic species inside the tumor, typically following removal of a protecting group. In contrast, TME-based catalytic strategies do not rely on the introduction of foreign species and instead use molecules that are already present in the TME. So far, only four processes have been explored in relation to cancer therapy, two oxidation reactions (glucose and glutathione), generation of reactive oxygen species (ROS) and production of oxygen to alleviate tumor hypoxia. This is surprising, since the rich chemical environment in tumor cells could in principle provide many other therapeutic opportunities. In particular, amino groups seem a suitable target, given the abundance of proteins and peptides in biological environments. Here we show that catalytic CuFe nanoparticles are able to foster transamination reactions between different amino acids and pyruvate, another key molecule that abounds in the TME. Transamination would then reduce the available aminoacid pool, which is likely to affect cell homeostasis and to effectively hinder tumor proliferation. After internalization of Cu-containing nanoparticles in U251-MG cells, we observed a significant decrease in glutamine and alanine levels up to 48 hours after treatment. In addition, we have found that not only simple amino acids, but also di- and tri-peptides undergo catalytic transamination when exposed to the Cu cations released by our nanoparticles, thus extending the range of the effects to other molecules such as GSSG. Mechanistic calculations for GSSG transamination revealed the formation of an imine between the oxo-group of pyruvate and the free -NH2 group of GSSG, followed by the coordination of the imine to Cu(II). Our results demonstrate that transamination reactions can be catalyzed in cellulo by Cu-releasing nanoparticles, adding a new reaction to the existing toolbox of catalytic therapies.

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AQME: Automated Quantum Mechanical Environments for Researchers and Educators

Cited by 3 publications

References 37 publications

AQME: Automated quantum mechanical environments for researchers and educators

AQME: Automated quantum mechanical environments for researchers and educators

Experimental and computational studies of the production of 1,3-butadiene from 2,3-butanediol using SiO2-supported H3PO4 derivatives

Cu-releasing nanoparticles induce the catalytic transamination of amino acids and GSSG under tumor microenvironment conditions

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