Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Varela-Rial A"'
Autor:
Galvelis, Raimondas, Varela-Rial, Alejandro, Doerr, Stefan, Fino, Roberto, Eastman, Peter, Markland, Thomas E., Chodera, John D., De Fabritiis, Gianni
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to traditiona
Externí odkaz:
http://arxiv.org/abs/2201.08110
Autor:
Marcel Proske, Robert Janowski, Sabrina Bacher, Hyun-Seo Kang, Thomas Monecke, Tony Koehler, Saskia Hutten, Jana Tretter, Anna Crois, Lena Molitor, Alejandro Varela-Rial, Roberto Fino, Elisa Donati, Gianni De Fabritiis, Dorothee Dormann, Michael Sattler, Dierk Niessing
Publikováno v:
eLife, Vol 13 (2024)
Mutations in the human PURA gene cause the neurodevelopmental PURA syndrome. In contrast to several other monogenetic disorders, almost all reported mutations in this nucleic acid-binding protein result in the full disease penetrance. In this study,
Externí odkaz:
https://doaj.org/article/13d881e49c7c4676869b2c6bc60d831e
Autor:
Varela-Rial, Alejandro, Majewski, Maciej, Cuzzolin, Alberto, Martínez-Rosell, Gerard, De Fabritiis, Gianni
SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge
Externí odkaz:
http://arxiv.org/abs/2005.05606
Publikováno v:
Artificial Intelligence Chemistry, Vol 1, Iss 2, Pp 100020- (2023)
Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of propertie
Externí odkaz:
https://doaj.org/article/2576a3549b194a7eb262504248cad2d8
Autor:
Schapin, Nikolai, Majewski, Maciej, Varela-Rial, Alejandro, Arroniz, Carlos, Fabritiis, Gianni De
Publikováno v:
In Artificial Intelligence Chemistry December 2023 1(2)
Akademický článek
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Akademický článek
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NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics
Autor:
Galvelis, Raimondas, Varela-Rial, Alejandro, Doerr, Stefan, Fino, Roberto, Eastman, Peter, Markland, Thomas E., Chodera, John D., De Fabritiis, Gianni
Parametric and non-parametric machine learning potentials have emerged recently as a way to improve the accuracy of bio-molecular simulations. Here, we present NNP/MM, an hybrid method integrating neural network potentials (NNPs) and molecular mechan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f65b3601a7aa8fad84407126d6cbee39
http://arxiv.org/abs/2201.08110
http://arxiv.org/abs/2201.08110
Autor:
Alejandro Varela-Rial, Iain Maryanow, Maciej Majewski, Stefan Doerr, Nikolai Schapin, José Jiménez-Luna, Gianni De Fabritiis
Publikováno v:
Journal of Chemical Information and Modeling
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d883c397f29fcb37c5a46ddfd4a0cbb
http://hdl.handle.net/10230/52307
http://hdl.handle.net/10230/52307
Autor:
Varela Rial, Alejandro, 1993
Publikováno v:
TDX (Tesis Doctorals en Xarxa)
TDR. Tesis Doctorales en Red
instname
TDR. Tesis Doctorales en Red
instname
The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with soft
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::c9ab0c5930fc7554efe829272ae4ba63
http://hdl.handle.net/10803/673579
http://hdl.handle.net/10803/673579