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ORCIDGianni De Fabritiis
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2020 – today
- 2024
[j34]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): 77 (2024)- [j33]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): 584-589 (2024) - [j32]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): 1481-1485 (2024) - [j31]Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro, 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. J. Chem. Inf. Model. 64(15): 5900-5911 (2024) - [c5]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 - [i22]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 abs/2402.17660 (2024) - [i21]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 abs/2403.15073 (2024) - [i20]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 abs/2405.04657 (2024) - [i19]Sebastian Dittert, Vincent Moens, Gianni De Fabritiis:
BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO. CoRR abs/2406.17490 (2024) - [i18]Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals, Gianni De Fabritiis:
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks. CoRR abs/2407.11103 (2024) - [i17]Nikolai Schapin, Carles Navarro, Albert Bou, Gianni De Fabritiis:
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction. CoRR abs/2407.19073 (2024) - [i16]Gianni De Fabritiis:
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations. CoRR abs/2408.12625 (2024) - [i15]Antonio Mirarchi, Raúl P. Peláez, Guillem Simeon, Gianni De Fabritiis:
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics. CoRR abs/2409.17852 (2024) - 2023
- [j30]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): 2438-2444 (2023) - [j29]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): 5701-5708 (2023) - [c4]Guillem Simeon, Gianni De Fabritiis:
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials. NeurIPS 2023 - [i14]Pablo Herrera-Nieto, Adrià Pérez, Gianni De Fabritiis:
Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics. CoRR abs/2302.10348 (2023) - [i13]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 abs/2306.00577 (2023) - [i12]Guillem Simeon, Gianni De Fabritiis:
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials. CoRR abs/2306.06482 (2023) - [i11]Carles Navarro, Maciej Majewski, Gianni De Fabritiis:
Top-down machine learning of coarse-grained protein force-fields. CoRR abs/2306.11375 (2023) - [i10]Nikolai Schapin, Maciej Majewski, Alejandro Varela-Rial, Carlos Arroniz, Gianni De Fabritiis:
Machine Learning Small Molecule Properties in Drug Discovery. CoRR abs/2308.12354 (2023) - [i9]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 abs/2310.03121 (2023) - 2022
- [j28]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): 225-231 (2022) - [c3]Philipp Thölke, Gianni De Fabritiis:
Equivariant Transformers for Neural Network based Molecular Potentials. ICLR 2022 - [i8]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 abs/2201.08110 (2022) - [i7]Philipp Thölke, Gianni De Fabritiis:
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials. CoRR abs/2202.02541 (2022) - [i6]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 abs/2209.10702 (2022) - [i5]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 abs/2212.07492 (2022) - 2021
- [c2]Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert:
Guided Exploration with Proximal Policy Optimization using a Single Demonstration. ICML 2021: 6611-6620 - 2020
- [j27]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): 1072 (2020) - [j26]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): 2314-2324 (2020) - [j25]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): 2673-2677 (2020) - [j24]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): 5003-5010 (2020) - [j23]Kenneth M. Merz Jr., Gianni De Fabritiis, Guo-Wei Wei:
Generative Models for Molecular Design. J. Chem. Inf. Model. 60(12): 5635-5636 (2020) - [i4]Albert Bou, Gianni De Fabritiis:
NAPPO: Modular and scalable reinforcement learning in pytorch. CoRR abs/2007.02622 (2020) - [i3]Gabriele Libardi, Gianni De Fabritiis:
Guided Exploration with Proximal Policy Optimization using a Single Demonstration. CoRR abs/2007.03328 (2020) - [i2]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 abs/2012.12106 (2020)
2010 – 2019
- 2019
- [j22]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): 243-250 (2019) - [j21]Miha Skalic, Gerard Martínez-Rosell, José Jiménez, Gianni De Fabritiis:
PlayMolecule BindScope: large scale CNN-based virtual screening on the web. Bioinform. 35(7): 1237-1238 (2019) - [j20]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): 1172-1181 (2019) - [j19]Miha Skalic, José Jiménez, Davide Sabbadin, Gianni De Fabritiis:
Shape-Based Generative Modeling for de Novo Drug Design. J. Chem. Inf. Model. 59(3): 1205-1214 (2019) - [j18]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): 3485-3493 (2019) - 2018
- [j17]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): 287-296 (2018) - [j16]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): 683-691 (2018) - 2017
- [j15]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): 3036-3042 (2017) - [j14]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): 1511-1516 (2017) - [i1]Stefan Doerr, Igor Ariz, Matthew J. Harvey, Gianni De Fabritiis:
Dimensionality reduction methods for molecular simulations. CoRR abs/1710.10629 (2017) - 2015
- [j13]Matt J. Harvey, Gianni De Fabritiis:
AceCloud: Molecular Dynamics Simulations in the Cloud. J. Chem. Inf. Model. 55(5): 909-914 (2015) - [j12]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): 2200-2205 (2015) - 2014
- [j11]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): 362-366 (2014) - [j10]Gianluigi 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): 2185-2189 (2014) - 2013
- [j9]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): 3123-3126 (2013) - 2011
- [j8]Matt J. Harvey, Gianni De Fabritiis:
Swan: A tool for porting CUDA programs to OpenCL. Comput. Phys. Commun. 182(4): 1093-1099 (2011) - 2010
- [j7]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): 1402-1409 (2010) - [j6]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): 869-878 (2010) - [j5]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): 397-403 (2010) - [j4]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)
2000 – 2009
- 2007
- [j3]Gianni De Fabritiis:
Performance of the Cell processor for biomolecular simulations. Comput. Phys. Commun. 176(11-12): 660-664 (2007) - 2006
- [j2]Peter V. Coveney, Gianni De Fabritiis, Matt J. Harvey, Stephen Pickles, Andrew R. Porter:
Coupled applications on distributed resources. Comput. Phys. Commun. 175(6): 389-396 (2006) - [j1]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): 190-194 (2006)
1990 – 1999
- 1999
- [c1]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: 103-111
Coauthor Index
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