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Molecular Simulation Volume 44, 2018 - Issue 11: Machine Learning

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Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

Joseph C. R. ThackerManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UK

Alex L. WilsonManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UK

Zak E. HughesManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UK

http://orcid.org/0000-0003-2166-9822

Matthew J. BurnManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UK

Peter I. MaxwellManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UK

Paul L. A. PopelierManchester Institute of Biotechnology (MIB), Manchester, UK;School of Chemistry, University of Manchester, Manchester, UKCorrespondence[email protected]

http://orcid.org/0000-0001-9053-1363

Pages 881-890 | Received 27 Oct 2017, Accepted 19 Jan 2018, Published online: 11 Feb 2018

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https://doi.org/10.1080/08927022.2018.1431837
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Fabio Falcioni & Paul L. A. Popelier. (2023) How to Compute Atomistic Insight in DFT Clusters: The REG-IQA Approach. Journal of Chemical Information and Modeling 63:14, pages 4312-4327.
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Joseph C. R. Thacker, David J. Bray, Patrick B. Warren & Richard L. Anderson. (2023) Can Machine Learning Predict the Phase Behavior of Surfactants?. The Journal of Physical Chemistry B 127:16, pages 3711-3727.
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Ángel Martín Pendás, Evelio Francisco, Dimas Suárez, Aurora Costales, Natalia Díaz, Julen Munárriz, Tomás Rocha-Rinza & José Manuel Guevara-Vela. (2023) Atoms in molecules in real space: a fertile field for chemical bonding. Physical Chemistry Chemical Physics 25:15, pages 10231-10262.
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Matthew J. Burn & Paul L. A. Popelier. (2023) Gaussian Process Regression Models for Predicting Atomic Energies and Multipole Moments. Journal of Chemical Theory and Computation 19:4, pages 1370-1380.
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Matthew J. Burn & Paul L. A. Popelier. (2023) FEREBUS: a high-performance modern Gaussian process regression engine. Digital Discovery 2:1, pages 152-164.
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Fabio Falcioni, Benjamin C.B. Symons & Paul L.A. Popelier. 2023.

César A. Zapata‐Acevedo, José Manuel Guevara‐Vela, Paul L. A. Popelier & Tomás Rocha‐Rinza. (2022) Binding Energy Partition of Promising IRAK‐4 Inhibitor (Zimlovisertib) for the Treatment of COVID‐19 Pneumonia. ChemPhysChem 23:24.
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Matthew J. Burn & Paul L. A. Popelier. (2022) Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation. Journal of Computational Chemistry 43:31, pages 2084-2098.
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Matthew J. Burn & Paul L. A. Popelier. (2022) ICHOR: a modern pipeline for producing Gaussian process regression models for atomistic simulations. Materials Advances 3:23, pages 8729-8739.
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Bruno Landeros-Rivera, Miguel Gallegos, Julen Munárriz, Rubén Laplaza & Julia Contreras-García. (2022) New venues in electron density analysis. Physical Chemistry Chemical Physics 24:36, pages 21538-21548.
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Benjamin C. B. Symons & Paul L. A. Popelier. (2022) Application of Quantum Chemical Topology Force Field FFLUX to Condensed Matter Simulations: Liquid Water. Journal of Chemical Theory and Computation 18:9, pages 5577-5588.
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Benjamin C. B. Symons, Michael K. Bane & Paul L. A. Popelier. (2021) DL_FFLUX: A Parallel, Quantum Chemical Topology Force Field. Journal of Chemical Theory and Computation 17:11, pages 7043-7055.
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Jack Broad, Simon Preston, Richard J. Wheatley & Richard S. Graham. (2021) Gaussian process models of potential energy surfaces with boundary optimization. The Journal of Chemical Physics 155:14.
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Pascal Friederich, Florian Häse, Jonny Proppe & Alán Aspuru-Guzik. (2021) Machine-learned potentials for next-generation matter simulations. Nature Materials 20:6, pages 750-761.
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Anton Konovalov, Benjamin C.B. Symons & Paul L.A. Popelier. (2020) On the many‐body nature of intramolecular forces in FFLUX and its implications . Journal of Computational Chemistry 42:2, pages 107-116.
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Yongna Yuan, Yan Ma, Dongxu Huo, Matthew J. L. Mills, Jiaxuan Wei, Wei Su & Ruisheng Zhang. (2020) Multipolar Description of Atom–Atom Electrostatic Interaction Energies in Single/Double-Stranded DNAs. The Journal of Physical Chemistry B 124:45, pages 10089-10103.
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Angelika Jabłuszewska, Anna Krawczuk, Leonardo H. R. Dos Santos & Piero Macchi. (2020) Accurate Modelling of Group Electrostatic Potential and Distributed Polarizability in Dipeptides. ChemPhysChem 21:19, pages 2155-2165.
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Krzysztof B. Beć, Justyna Grabska & Christian W. Huck. (2020) Biomolecular and bioanalytical applications of infrared spectroscopy – A review. Analytica Chimica Acta 1133, pages 150-177.
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José Manuel Guevara-Vela, Evelio Francisco, Tomás Rocha-Rinza & Ángel Martín Pendás. (2020) Interacting Quantum Atoms—A Review. Molecules 25:17, pages 4028.
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Matthew J. Burn & Paul L. A. Popelier. (2020) Creating Gaussian process regression models for molecular simulations using adaptive sampling. The Journal of Chemical Physics 153:5.
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Arnaldo F. Silva, Leonardo J. Duarte & Paul L. A. Popelier. (2020) Contributions of IQA electron correlation in understanding the chemical bond and non-covalent interactions. Structural Chemistry 31:2, pages 507-519.
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Zak E. Hughes, Emmanuel Ren, Joseph C. R. Thacker, Benjamin C. B. Symons, Arnaldo F. Silva & Paul L. A. Popelier. (2019) A FFLUX Water Model: Flexible, Polarizable and with a Multipolar Description of Electrostatics. Journal of Computational Chemistry 41:7, pages 619-628.
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Mark A. Vincent, Arnaldo F. Silva & Paul L. A. Popelier. (2019) Atomic Partitioning of the MPn ( n = 2, 3, 4) Dynamic Electron Correlation Energy by the Interacting Quantum Atoms Method: A Fast and Accurate Electrostatic Potential Integral Approach . Journal of Computational Chemistry 40:32, pages 2793-2800.
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Adam S. Abbott, Justin M. Turney, Boyi Zhang, Daniel G. A. Smith, Doaa Altarawy & Henry F. SchaeferIIIIII. (2019) PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces. Journal of Chemical Theory and Computation 15:8, pages 4386-4398.
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Zak E. Hughes, Joseph C. R. ThackerAlex L. WilsonPaul L. A. Popelier. (2018) Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models. Journal of Chemical Theory and Computation 15:1, pages 116-126.
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Yongna Yuan, Zhuangzhuang Zhang, Matthew J. L. Mills, Rongjing Hu & Ruisheng Zhang. (2018) Assessing Force Field Potential Energy Function Accuracy via a Multipolar Description of Atomic Electrostatic Interactions in RNA. Journal of Chemical Information and Modeling 58:11, pages 2239-2254.
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Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

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Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

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