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DC Field | Value | Language |
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dc.contributor.author | Marchetti, F | - |
dc.contributor.author | Moroni, E | - |
dc.contributor.author | Pandini, A | - |
dc.contributor.author | Colombo, G | - |
dc.date.accessioned | 2021-05-09T16:55:04Z | - |
dc.date.available | 2021-05-09T16:55:04Z | - |
dc.date.issued | 2021-04-12 | - |
dc.identifier.citation | Marchetti, F., Moroni, E., Pandini, A. and Colombo, G. (2021) 'Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics', The Journal of Physical Chemistry Letters, 12(15), pp. 3724-3732. doi: 10.1021/acs.jpclett.1c00045. | en_US |
dc.identifier.issn | 1948-7185 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22635 | - |
dc.description.abstract | © 2021 The Authors. Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein’s function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins. | en_US |
dc.description.sponsorship | AIRC IG 2017 - ID. 20019 project; AIRC Fellowship; EC Research Innovation Action H2020 Programme Project HPC-EUROPA3 (INFRAIA-2016-1-730897); | en_US |
dc.format.extent | 3724 - 3732 | - |
dc.format.medium | Print-Electronic | - |
dc.language | en | - |
dc.language.iso | en_US | en_US |
dc.publisher | American Chemical Society (ACS) | en_US |
dc.rights | Copyright © 2021 The Authors. Published by American Chemical Society under a CC BY Creative Commons license | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.title | Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1021/acs.jpclett.1c00045 | - |
dc.relation.isPartOf | The Journal of Physical Chemistry Letters | - |
pubs.publication-status | Published online | - |
dc.identifier.eissn | 1948-7185 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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