Výsledky vyhledávání - "Binding free energy prediction"

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including Long-range Effects

Autor: Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, Pengyu Ren, Hatice Gökcan, Olexandr Isayev, Louis Lagardère, Jean-Philip Piquemal

Publikováno v: Chemical Science
Chemical Science, 2023, ⟨10.1039/D2SC04815A⟩

International audience; Deep-HP is a scalable extension of the Tinker-HP multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders of magnitu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3204c37f45afbf96b891d166d560cc0
https://hal.science/hal-03738403

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Exploration computationnelle de complexes hôte-invité

Autor: Serillon, Dylan

Supramolecular chemistry has experienced enormous growth in recent years. Supramolecular processes and, in particular, host-guest interactions are studied for the variety of their potential applications (from industrial processes to medical field app

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______166::e98ec6355333ac61e7fcd6a9f66bf631
https://theses.hal.science/tel-03703510

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Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation

Autor: Daan P. Geerke, Nico P. E. Vermeulen, Marc van Dijk, Eko Aditya Rifai

Publikováno v: Journal of Computer-aided Molecular Design
Journal of Computer-Aided Molecular Design
Journal of Computer-aided Molecular Design, 32(1), 239-249. Springer Netherlands
Rifai, E A, van Dijk, M, Vermeulen, N P E & Geerke, D P 2017, ' Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation : application to the D3R Grand Challenge 2 ', Journal of Computer-aided Molecular Design, vol. 32, no. 1, pp. 239-249 . https://doi.org/10.1007/s10822-017-0055-0

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::580c9a18f6878d4971290234f09a515f
https://doi.org/10.1007/s10822-017-0055-0

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Improving the iterative linear interaction energy approach using automated recognition of configurational transitions

Autor: Luigi Capoferri, Daan P. Geerke, Derk P. Kooi, C. Ruben Vosmeer, Nico P. E. Vermeulen, Margreet M. Terpstra

Publikováno v: Journal of Molecular Modeling, 1:31. Springer Verlag
Vosmeer, C R, Kooi, D P, Capoferri, L, Terpstra, M M, Vermeulen, N P E & Geerke, D P 2016, ' Improving the iterative linear interaction energy approach using automated recognition of configurational transitions. ', Journal of Molecular Modeling, vol. 1, 31 . https://doi.org/10.1007/s00894-015-2883-y
Journal of Molecular Modeling

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was sho

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e76eb8c0d075d8248f4ae8ef2b5b008
https://research.vu.nl/en/publications/68d814af-97bd-4c5b-9720-d68fca3e2c30

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Assessing protein kinase selectivity with molecular dynamics and MM-PBSA binding free energy calculations

Autor: Elena, Muzzioli, Alberto, Del Rio, Giulio, Rastelli

An application of molecular dynamics and molecular mechanics Poisson-Boltzmann surface area techniques to the prediction of protein kinase inhibitor selectivity is presented. A highly active and selective ERK2 inhibitor was placed in equivalent orien

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::8c245d6dc9951af70da70186eaacc705
Wiley Online Library

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