Gianni De Fabritiis

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 44

Top Topics

Molecular Dynamics

Top Venues

J. Chem. Inf. Model.

Publications

Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro Ramírez, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen Ahmad, Vincent Moens, Woody Sherman, Simone Sciabola, Gianni De Fabritiis
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery. CoRR (2024)
Guillem Simeon, Antonio Mirarchi, Raúl P. Peláez, Raimondas Galvelis, Gianni De Fabritiis
On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials. CoRR (2024)
Sebastian Dittert, Vincent Moens, Gianni De Fabritiis
BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO. CoRR (2024)
Francesc Sabanés Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis
Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J. Chem. Inf. Model. 64 (5) (2024)
Mariona Torrens-Fontanals, Panagiotis Tourlas, Stefan Doerr, Gianni De Fabritiis
PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data. J. Chem. Inf. Model. 64 (3) (2024)
Raúl P. Peláez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter K. Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. CoRR (2024)
Morgan Thomas, Mazen Ahmad, Gary Tresadern, Gianni De Fabritiis
PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. J. Cheminformatics 16 (1) (2024)
Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals, Gianni De Fabritiis
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks. CoRR (2024)
Nikolai Schapin, Carles Navarro, Albert Bou, Gianni De Fabritiis
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction. CoRR (2024)
Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens
TorchRL: A data-driven decision-making library for PyTorch. ICLR (2024)
Peter K. Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João P. G. L. M. Rodrigues, Jaime Rodríguez-Guerra, Andrew C. Simmonett, Jason M. Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. CoRR (2023)
Nikolai Schapin, Maciej Majewski, Alejandro Varela-Rial, Carlos Arroniz, Gianni De Fabritiis
Machine Learning Small Molecule Properties in Drug Discovery. CoRR (2023)
Carles Navarro, Maciej Majewski, Gianni De Fabritiis
Top-down machine learning of coarse-grained protein force-fields. CoRR (2023)
Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens
TorchRL: A data-driven decision-making library for PyTorch. CoRR (2023)
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. J. Chem. Inf. Model. 63 (18) (2023)
Guillem Simeon, Gianni De Fabritiis
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials. CoRR (2023)
Pablo Herrera-Nieto, Adrià Pérez, Gianni De Fabritiis
Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics. CoRR (2023)
Guillem Simeon, Gianni De Fabritiis
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials. NeurIPS (2023)
Francesc Sabanés Zariquiey, Adrià Pérez, Maciej Majewski, Emilio Gallicchio, Gianni De Fabritiis
Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series. J. Chem. Inf. Model. 63 (8) (2023)
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. CoRR (2022)
Peter K. Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, Thomas E. Markland
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. CoRR (2022)
Alejandro Varela-Rial, Iain Maryanow, Maciej Majewski, Stefan Doerr, Nikolai Schapin, José Jiménez-Luna, Gianni De Fabritiis
PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. J. Chem. Inf. Model. 62 (2) (2022)
Philipp Thölke, Gianni De Fabritiis
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials. CoRR (2022)
Philipp Thölke, Gianni De Fabritiis
Equivariant Transformers for Neural Network based Molecular Potentials. ICLR (2022)
Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. CoRR (2022)
Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert
Guided Exploration with Proximal Policy Optimization using a Single Demonstration. ICML (2021)
Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noé, Toni Giorgino, Gianni De Fabritiis
TorchMD: A deep learning framework for molecular simulations. CoRR (2020)
Kenneth M. Merz Jr., Gianni De Fabritiis, Guo-Wei Wei
Generative Models for Molecular Design. J. Chem. Inf. Model. 60 (12) (2020)
Alejandro Varela-Rial, Maciej Majewski, Alberto Cuzzolin, Gerard Martínez-Rosell, Gianni De Fabritiis
SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J. Chem. Inf. Model. 60 (6) (2020)
Pablo Herrera-Nieto, Adrià Pérez, Gianni De Fabritiis
Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations. J. Chem. Inf. Model. 60 (10) (2020)
Gabriele Libardi, Gianni De Fabritiis
Guided Exploration with Proximal Policy Optimization using a Single Demonstration. CoRR (2020)
Kenneth M. Merz Jr., Gianni De Fabritiis, Guo-Wei Wei
JCIM Special Issue on Generative Models for Molecular Design. J. Chem. Inf. Model. 60 (3) (2020)
Albert Bou, Gianni De Fabritiis
NAPPO: Modular and scalable reinforcement learning in pytorch. CoRR (2020)
Gerard Martínez-Rosell, Silvia Lovera, Zara A. Sands, Gianni De Fabritiis
PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations. J. Chem. Inf. Model. 60 (4) (2020)
Miha Skalic, Alejandro Varela-Rial, José Jiménez, Gerard Martínez-Rosell, Gianni De Fabritiis
LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinform. 35 (2) (2019)
José Jiménez, Davide Sabbadin, Alberto Cuzzolin, Gerard Martínez-Rosell, Jacob Gora, John Manchester, José S. Duca, Gianni De Fabritiis
PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. J. Chem. Inf. Model. 59 (3) (2019)
Miha Skalic, Gerard Martínez-Rosell, José Jiménez, Gianni De Fabritiis
PlayMolecule BindScope: large scale CNN-based virtual screening on the web. Bioinform. 35 (7) (2019)
Raimondas Galvelis, Stefan Doerr, João M. Damas, Matt J. Harvey, Gianni De Fabritiis
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning. J. Chem. Inf. Model. 59 (8) (2019)
Miha Skalic, José Jiménez, Davide Sabbadin, Gianni De Fabritiis
Shape-Based Generative Modeling for de Novo Drug Design. J. Chem. Inf. Model. 59 (3) (2019)
Gerard Martínez-Rosell, Matt J. Harvey, Gianni De Fabritiis
Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors. J. Chem. Inf. Model. 58 (3) (2018)
José Jiménez, Miha Skalic, Gerard Martínez-Rosell, Gianni De Fabritiis
KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. J. Chem. Inf. Model. 58 (2) (2018)
Stefan Doerr, Igor Ariz, Matthew J. Harvey, Gianni De Fabritiis
Dimensionality reduction methods for molecular simulations. CoRR (2017)
Gerard Martínez-Rosell, Toni Giorgino, Gianni De Fabritiis
PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations. J. Chem. Inf. Model. 57 (7) (2017)
José Jiménez, Stefan Doerr, Gerard Martínez-Rosell, Alexander S. Rose, Gianni De Fabritiis
DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinform. 33 (19) (2017)
Noelia Ferruz, Matthew J. Harvey, Jordi Mestres, Gianni De Fabritiis
Insights from Fragment Hit Binding Assays by Molecular Simulations. J. Chem. Inf. Model. 55 (10) (2015)
Matt J. Harvey, Gianni De Fabritiis
AceCloud: Molecular Dynamics Simulations in the Cloud. J. Chem. Inf. Model. 55 (5) (2015)
G. Lauro, Noelia Ferruz, Simone Fulle, Matt J. Harvey, Paul W. Finn, Gianni De Fabritiis
Reranking Docking Poses Using Molecular Simulations and Approximate Free Energy Methods. J. Chem. Inf. Model. 54 (8) (2014)
Paola Bisignano, Stefan Doerr, Matt J. Harvey, Angelo D. Favia, Andrea Cavalli, Gianni De Fabritiis
Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations. J. Chem. Inf. Model. 54 (2) (2014)
Ignasi Buch, Noelia Ferruz, Gianni De Fabritiis
Computational Modeling of an Epidermal Growth Factor Receptor Single-Mutation Resistance to Cetuximab in Colorectal Cancer Treatment. J. Chem. Inf. Model. 53 (12) (2013)
Matt J. Harvey, Gianni De Fabritiis
Swan: A tool for porting CUDA programs to OpenCL. Comput. Phys. Commun. 182 (4) (2011)
Toni Giorgino, Matt J. Harvey, Gianni De Fabritiis
Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations. Comput. Phys. Commun. 181 (8) (2010)
Ignasi Buch, Matt J. Harvey, Toni Giorgino, David P. Anderson, Gianni De Fabritiis
High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. J. Chem. Inf. Model. 50 (3) (2010)
Jana Selent, Ferran Sanz, Manuel Pastor, Gianni De Fabritiis
Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors. PLoS Comput. Biol. 6 (8) (2010)
Adrián López García de Lomana, Qasim K. Beg, Gianni De Fabritiis, Jordi Villà-Freixa
Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks. J. Comput. Biol. 17 (7) (2010)
Gianni De Fabritiis
Performance of the Cell processor for biomolecular simulations. Comput. Phys. Commun. 176 (11-12) (2007)
Peter V. Coveney, Gianni De Fabritiis, Matt J. Harvey, Stephen Pickles, A. R. Porter
Coupled applications on distributed resources. Comput. Phys. Commun. 175 (6) (2006)
Mar Serrano, Gianni De Fabritiis, Pep Español, Peter V. Coveney
A stochastic Trotter integration scheme for dissipative particle dynamics. Math. Comput. Simul. 72 (2-6) (2006)
Giovanni Erbacci, Gianni De Fabritiis, Gaetano Bellanca, Paolo Bassi, R. Roccari
Performance evaluation of a FD-TD parallel code for microwave ovens design. PARCO (1999)