G. D. Fabritiis

发表

Dynamical geometry for multiscale dissipative particle dynamics

P. Coveney, G. D. Fabritiis, 2003, cond-mat/0301378.

The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor

L. Pardo, G. D. Fabritiis, Nathaniel H. Stanley, 2016, Scientific Reports.

Efficient numerical integrators for stochastic models

P. Español, G. D. Fabritiis, M. Serrano, 2005, physics/0502064.

PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks

G. D. Fabritiis, S. Doerr, Maciej Majewski, 2022, J. Chem. Inf. Model..

Discrete random walk models for symmetric Lévy–Feller diffusion processes

H Germany, Italy., R. Gorenflo, 1999, cond-mat/9903264.

Coupled applications on distributed resources

P. Coveney, A. Porter, M. Harvey, 2006, Comput. Phys. Commun..

Efficient numerical integrators for stochastic models

P. Español, G. D. Fabritiis, M. Serrano, 2005, physics/0502064.

A stochastic Trotter integration scheme for dissipative particle dynamics

P. Coveney, P. Español, G. D. Fabritiis, 2006, Math. Comput. Simul..

JCIM Special Issue on Generative Models for Molecular Design

Gianni De Fabritiis, Kenneth M. Merz, Guowei Wei, 2020, Journal of Chemical Information and Modeling.

TorchMD: A Deep Learning Framework for Molecular Simulations

Toni Giorgino, Frank Noé, Cecilia Clementi, 2020, Journal of Chemical Theory and Computation.

INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND GENERAL J. Phys. A: Math. Gen. 35 (2002) 1605–1625 PII: S0305-4470(02)28700-4

Pep Español, Mar Serrano, Gianni De Fabritiis, 2022 .

Dynamical geometry for multiscale dissipative particle dynamics

P. Coveney, G. D. Fabritiis, 2003, cond-mat/0301378.

Performance evaluation of a FD-TD parallel code for microwave ovens design

Gianni De Fabritiis, Giovanni Erbacci, G. Bellanca, 2000, PARCO.

Machine learning coarse-grained potentials of protein thermodynamics

G. D. Fabritiis, C. Clementi, S. Doerr, 2022, Nature communications.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Benjamin P. Pritchard, David L. Dotson, John E. Herr, 2022, Scientific Data.

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

Gianni De Fabritiis, Matt J. Harvey, Stefan Doerr, 2019, J. Chem. Inf. Model..

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale.

M J Harvey, G Giupponi, G De Fabritiis, 2009, Journal of chemical theory and computation.

DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks

Gianni De Fabritiis, Alexander S. Rose, Gerard Martínez-Rosell, 2017, Bioinform..

Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks

Adrián López García de Lomana, Jordi Villà-Freixa, Gianni De Fabritiis, 2010, J. Comput. Biol..

TorchMD: A Deep Learning Framework for Molecular Simulations pdf

Toni Giorgino, Frank Noé, Cecilia Clementi, 2020, Journal of chemical theory and computation.

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Gianni De Fabritiis, Gerard Martínez-Rosell, Miha Skalic, 2018, J. Chem. Inf. Model..

Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations

Toni Giorgino, Gianni De Fabritiis, Matt J. Harvey, 2010, Comput. Phys. Commun..

A coupled molecular-continuum hybrid model for the simulation of macromolecular dynamics

Gianni De Fabritiis, Peter V. Coveney, G. Giupponi, 2007 .

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors

Gianni De Fabritiis, Matt J. Harvey, Gerard Martínez-Rosell, 2018, J. Chem. Inf. Model..

Insights from Fragment Hit Binding Assays by Molecular Simulations

Jordi Mestres, Gianni De Fabritiis, Matt J. Harvey, 2015, J. Chem. Inf. Model..

Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations

Andrea Cavalli, Paola Bisignano, Matthew J. Harvey, 2014, J. Chem. Inf. Model..

Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations

Gianni De Fabritiis, Adrià Pérez, Pablo Herrera-Nieto, 2020, J. Chem. Inf. Model..

Swan: A tool for porting CUDA programs to OpenCL

Gianni De Fabritiis, Matt J. Harvey, M. Harvey, 2011, Comput. Phys. Commun..

Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Gianni De Fabritiis, Gabriele Libardi, G. D. Fabritiis, 2020, ICML.

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks

Gianni De Fabritiis, Gerard Martínez-Rosell, Davide Sabbadin, 2018, J. Chem. Inf. Model..

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

G. D. Fabritiis, Philipp Thölke, 2022, ICLR.

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

David P. Anderson, Toni Giorgino, Gianni De Fabritiis, 2010, J. Chem. Inf. Model..

Shape-Based Generative Modeling for de Novo Drug Design

Gianni De Fabritiis, Davide Sabbadin, Miha Skalic, 2019, J. Chem. Inf. Model..

On Size and Growth of Business Firms

F. Pammolli, G. D. Fabritiis, M. Riccaboni, 2003, cond-mat/0307074.

PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations

Gianni De Fabritiis, Gerard Martínez-Rosell, Silvia Lovera, 2020, J. Chem. Inf. Model..

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials

G. D. Fabritiis, Guillem Simeon, 2023, ArXiv.

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series

E. Gallicchio, G. D. Fabritiis, Francesc Sabanés Zariquiey, 2023, J. Chem. Inf. Model..

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

E. Gallicchio, G. D. Fabritiis, Francesc Sabanés Zariquiey, 2023, ArXiv.

Mesoscopic Models of Liquid/Solid Phase Transitions

S. Succi, A. Mancini, G. D. Fabritiis, 1998 .

Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors

Gianni De Fabritiis, Ferran Sanz, Manuel Pastor, 2010, PLoS Comput. Biol..

PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations

Toni Giorgino, Gianni De Fabritiis, Gerard Martínez-Rosell, 2017, J. Chem. Inf. Model..

Top-down machine learning of coarse-grained protein force-fields

G. D. Fabritiis, Maciej Majewski, Cales Navarro, 2023, ArXiv.

Reranking Docking Poses Using Molecular Simulations and Approximate Free Energy Methods

Gianni De Fabritiis, Matt J. Harvey, Simone Fulle, 2014, J. Chem. Inf. Model..

TorchRL: A data-driven decision-making library for PyTorch

Shagun Sodhani, G. D. Fabritiis, V. Moens, 2023, ArXiv.

The impact of accelerator processors for high-throughput molecular modeling and simulation.

M J Harvey, G De Fabritiis, G Giupponi, 2008, Drug discovery today.

On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.

S. Doerr, G. De Fabritiis, G. D. Fabritiis, 2014, Journal of chemical theory and computation.

Machine Learning Small Molecule Properties in Drug Discovery

G. D. Fabritiis, Maciej Majewski, Alejandro Varela-Rial, 2023, Artificial Intelligence Chemistry.

Multiscale dissipative particle dynamics.

Gianni De Fabritiis, G. D. Fabritiis, 2002 .

Generative Models for Molecular Design

G. Wei, K. Merz, G. D. Fabritiis, 2020, J. Chem. Inf. Model..

PlayMolecule BindScope: large scale CNN-based virtual screening on the web

Gianni De Fabritiis, Gerard Martínez-Rosell, José Jiménez, 2018, Bioinform..

An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware.

M J Harvey, G De Fabritiis, M. Harvey, 2009, Journal of chemical theory and computation.

LigVoxel: inpainting binding pockets using 3D‐convolutional neural networks

Gianni De Fabritiis, Gerard Martínez-Rosell, José Jiménez, 2018, Bioinform..

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

Frank Noé, G De Fabritiis, S Doerr, 2016, Journal of chemical theory and computation.

Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics

G. D. Fabritiis, Pablo Herrera-Nieto, Adria P'erez, 2023, Journal of chemical theory and computation.

SkeleDock: A Web Application for Scaffold Docking in PlayMolecule

Maciej Majewski, Gianni De Fabritiis, Gerard Martínez-Rosell, 2020, J. Chem. Inf. Model..

Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks

G. D. Fabritiis, A. L. G. D. Lomana, Jordi Villà-Freixa, 2010, J. Comput. Biol..

Computational Modeling of an Epidermal Growth Factor Receptor Single-Mutation Resistance to Cetuximab in Colorectal Cancer Treatment

Gianni De Fabritiis, Noelia Ferruz, Ignasi Buch, 2013, J. Chem. Inf. Model..

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

F. Noé, M. Harvey, G. D. Fabritiis, 2016, Journal of chemical theory and computation.

PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations

G. D. Fabritiis, T. Giorgino, Gerard Martínez-Rosell, 2017, J. Chem. Inf. Model..

AceCloud: Molecular Dynamics Simulations in the Cloud

Gianni De Fabritiis, Matt J. Harvey, M. Harvey, 2015, J. Chem. Inf. Model..

Molecular modeling and simulation of membrane lipid-mediated effects on GPCRs.

M. Pastor, G. D. Fabritiis, G. de Fabritiis, 2012, Current medicinal chemistry.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Andrew C. Simmonett, J. Chodera, Ivy Zhang, 2023, ArXiv.

Computational modeling of an EGFR single-mutation resistance to cetuximab in colorectal cancer treatment

G. D. Fabritiis, I. Buch, Noelia Ferruz, 2014 .

A survey of computational molecular science using graphics processing units

Gianni De Fabritiis, M. J. Harvey, G. D. Fabritiis, 2012 .

Performance of the Cell processor for biomolecular simulations

G. D. Fabritiis, 2006, Computer Physics Communications.

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

J. Chodera, G. D. Fabritiis, R. Fino, 2022, J. Chem. Inf. Model..

Energetics of K+ permeability through Gramicidin A by forward‐reverse steered molecular dynamics

G De Fabritiis, P V Coveney, P. Coveney, 2008, Proteins.

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Gianni De Fabritiis, Roberto Fino, John D. Chodera, 2022, ArXiv.

Foundations of dissipative particle dynamics

P. Coveney, E. Flekkøy, G. D. Fabritiis, 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

Navigating protein landscapes with a machine-learned transferable coarse-grained model

Aleksander E. P. Durumeric, G. D. Fabritiis, C. Clementi, 2023, 2310.18278.

GPCRmd uncovers the dynamics of the 3D-GPCRome

David E. Gloriam, Johanna K. S. Tiemann, J. C. Gómez-Tamayo, 2020 .

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Andrew C. Simmonett, J. Chodera, Ivy Zhang, 2023, ArXiv.

PlayMolecule Parameterize: a Scalable Molecular Force Field Parameterization Method Based on Quantum-Level Machine Learning

M. Harvey, G. D. Fabritiis, G. de Fabritiis, 2019 .

PURA syndrome-causing mutations impair PUR-domain integrity and affect P-body association

R. Janowski, G. D. Fabritiis, Michael Sattler, 2024, bioRxiv.

PlayMolecule BindScope: large scale CNN-based virtual screening on the web

G. D. Fabritiis, M. Škalič, Gerard Martínez-Rosell, 2018, Bioinform..