Simulations meet machine learning in structural biology

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  • dc.contributor.author Pérez, Adrià
  • dc.contributor.author Martínez Rosell, Gerard, 1990-
  • dc.contributor.author De Fabritiis, Gianni
  • dc.date.accessioned 2019-05-15T07:54:12Z
  • dc.date.available 2019-05-15T07:54:12Z
  • dc.date.issued 2018
  • dc.description.abstract Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. We envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery.
  • dc.description.sponsorship The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from MINECO (BIO2017-82628-P) and FEDER, as well as ‘Unidad de Excelencia María de Maeztu’, funded by MINECO (MDM-2014-0370). The authors thank the European Union's Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Pérez A, Martínez-Rosell G, De Fabritiis G. Simulations meet machine learning in structural biology. Curr Opin Struct Biol. 2018 Feb 21;49:139-44. DOI: 10.1016/j.sbi.2018.02.004
  • dc.identifier.doi http://dx.doi.org/10.1016/j.sbi.2018.02.004
  • dc.identifier.issn 0959-440X
  • dc.identifier.uri http://hdl.handle.net/10230/37227
  • dc.language.iso eng
  • dc.publisher Elsevier
  • dc.relation.ispartof Current Opinion in Structural Biology. 2018 Feb 21;49:139-44
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/675451
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/675451
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/BIO2017-82628-P
  • dc.rights © Elsevier http://dx.doi.org/10.1016/j.sbi.2018.02.004
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.title Simulations meet machine learning in structural biology
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/acceptedVersion

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