Gianni De Fabritiis
Overview
Explore the profile of Gianni De Fabritiis including associated specialties, affiliations and a list of published articles.
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89
Citations
2440
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Recent Articles
1.
Simeon G, Mirarchi A, Pelaez R, Galvelis R, De Fabritiis G
J Chem Theory Comput
. 2025 Feb;
21(4):1831-1837.
PMID: 39933873
Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design. In this work, we demonstrate...
2.
Mirarchi A, Giorgino T, De Fabritiis G
ArXiv
. 2024 Dec;
PMID: 39679266
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins,...
3.
Mirarchi A, Giorgino T, De Fabritiis G
Sci Data
. 2024 Nov;
11(1):1299.
PMID: 39609442
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins,...
4.
Mirarchi A, Pelaez R, Simeon G, De Fabritiis G
J Chem Theory Comput
. 2024 Nov;
20(22):9871-9878.
PMID: 39514694
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a...
5.
Bou A, Thomas M, Dittert S, Navarro C, Majewski M, Wang Y, et al.
J Chem Inf Model
. 2024 Aug;
64(15):5900-5911.
PMID: 39092857
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance...
6.
Thomas M, Ahmad M, Tresadern G, De Fabritiis G
J Cheminform
. 2024 Jul;
16(1):77.
PMID: 38965600
SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and...
7.
Pelaez R, Simeon G, Galvelis R, Mirarchi A, Eastman P, Doerr S, et al.
J Chem Theory Comput
. 2024 May;
20(10):4076-4087.
PMID: 38743033
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step...
8.
Proske M, Janowski R, Bacher S, Kang H, Monecke T, Koehler T, et al.
Elife
. 2024 Apr;
13.
PMID: 38655849
Mutations in the human 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...
9.
Pelaez R, Simeon G, Galvelis R, Mirarchi A, Eastman P, Doerr S, et al.
ArXiv
. 2024 Mar;
PMID: 38463504
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal...
10.
Sabanes Zariquiey F, Galvelis R, Gallicchio E, Chodera J, Markland T, De Fabritiis G
J Chem Inf Model
. 2024 Feb;
64(5):1481-1485.
PMID: 38376463
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM)....