Performance evaluation of a FD-TD parallel code for microwave ovens design. - Giovanni Erbacci; Gianni De Fabritiis; Gaetano Bellanca; Paolo Bassi; R. Roccari (PARCO, 1999)

Coupled applications on distributed resources. - Peter V. Coveney; Gianni De Fabritiis; Matt J. Harvey; Stephen Pickles; Andrew R. Porter (Comput. Phys. Commun., 2006)

A stochastic Trotter integration scheme for dissipative particle dynamics. - Mar Serrano; Gianni De Fabritiis; Pep Español; Peter V. Coveney (Math. Comput. Simul., 2006)

Performance of the Cell processor for biomolecular simulations. - Gianni De Fabritiis (Comput. Phys. Commun., 2007)

Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations. - Toni Giorgino; Matt J. Harvey; Gianni De Fabritiis (Comput. Phys. Commun., 2010)

Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks. - Adrián López García de Lomana; Qasim K. Beg; Gianni De Fabritiis; Jordi Villà-Freixa (J. Comput. Biol., 2010)

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. - Ignasi Buch; Matt J. Harvey; Toni Giorgino; David P. Anderson; Gianni De Fabritiis (J. Chem. Inf. Model., 2010)

Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors. - Jana Selent; Ferran Sanz; Manuel Pastor; Gianni De Fabritiis (PLoS Comput. Biol., 2010)

Swan: A tool for porting CUDA programs to OpenCL. - Matt J. Harvey; Gianni De Fabritiis (Comput. Phys. Commun., 2011)

Computational Modeling of an Epidermal Growth Factor Receptor Single-Mutation Resistance to Cetuximab in Colorectal Cancer Treatment. - Ignasi Buch; Noelia Ferruz; Gianni De Fabritiis (J. Chem. Inf. Model., 2013)

Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations. - Paola Bisignano; Stefan Doerr; Matt J. Harvey; Angelo D. Favia; Andrea Cavalli; Gianni De Fabritiis (J. Chem. Inf. Model., 2014)

Reranking Docking Poses Using Molecular Simulations and Approximate Free Energy Methods. - Gianluigi Lauro; Noelia Ferruz; Simone Fulle; Matt J. Harvey; Paul W. Finn; Gianni De Fabritiis (J. Chem. Inf. Model., 2014)

Insights from Fragment Hit Binding Assays by Molecular Simulations. - Noelia Ferruz; Matthew J. Harvey; Jordi Mestres; Gianni De Fabritiis (J. Chem. Inf. Model., 2015)

AceCloud: Molecular Dynamics Simulations in the Cloud. - Matt J. Harvey; Gianni De Fabritiis (J. Chem. Inf. Model., 2015)

DeepSite: protein-binding site predictor using 3D-convolutional neural networks. - José Jiménez; Stefan Doerr; Gerard Martínez-Rosell; Alexander S. Rose; Gianni De Fabritiis (Bioinform., 2017)

Dimensionality reduction methods for molecular simulations. - Stefan Doerr; Igor Ariz; Matthew J. Harvey; Gianni De Fabritiis (CoRR, 2017)

PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations. - Gerard Martínez-Rosell; Toni Giorgino; Gianni De Fabritiis (J. Chem. Inf. Model., 2017)

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. - José Jiménez; Miha Skalic; Gerard Martínez-Rosell; Gianni De Fabritiis (J. Chem. Inf. Model., 2018)

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors. - Gerard Martínez-Rosell; Matt J. Harvey; Gianni De Fabritiis (J. Chem. Inf. Model., 2018)

PlayMolecule BindScope: large scale CNN-based virtual screening on the web. - Miha Skalic; Gerard Martínez-Rosell; José Jiménez; Gianni De Fabritiis (Bioinform., 2019)

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. - Miha Skalic; Alejandro Varela-Rial; José Jiménez; Gerard Martínez-Rosell; Gianni De Fabritiis (Bioinform., 2019)

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning. - Raimondas Galvelis; Stefan Doerr; João M. Damas; Matt J. Harvey; Gianni De Fabritiis (J. Chem. Inf. Model., 2019)

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. - José Jiménez; Davide Sabbadin; Alberto Cuzzolin; Gerard Martínez-Rosell; Jacob Gora; John Manchester; José S. Duca; Gianni De Fabritiis (J. Chem. Inf. Model., 2019)

Shape-Based Generative Modeling for de Novo Drug Design. - Miha Skalic; José Jiménez; Davide Sabbadin; Gianni De Fabritiis (J. Chem. Inf. Model., 2019)

NAPPO: Modular and scalable reinforcement learning in pytorch. - Albert Bou; Gianni De Fabritiis (CoRR, 2020)

Guided Exploration with Proximal Policy Optimization using a Single Demonstration. - Gabriele Libardi; Gianni De Fabritiis (CoRR, 2020)

TorchMD: A deep learning framework for molecular simulations. - Stefan Doerr; Maciej Majewski; Adrià Pérez; Andreas Krämer; Cecilia Clementi; Frank Noé; Toni Giorgino; Gianni De Fabritiis (CoRR, 2020)

Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations. - Pablo Herrera-Nieto; Adrià Pérez; Gianni De Fabritiis (J. Chem. Inf. Model., 2020)

PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations. - Gerard Martínez-Rosell; Silvia Lovera; Zara A. Sands; Gianni De Fabritiis (J. Chem. Inf. Model., 2020)

JCIM Special Issue on Generative Models for Molecular Design. - Kenneth M. Merz Jr.; Gianni De Fabritiis; Guo-Wei Wei 001 (J. Chem. Inf. Model., 2020)

Generative Models for Molecular Design. - Kenneth M. Merz Jr.; Gianni De Fabritiis; Guo-Wei Wei 001 (J. Chem. Inf. Model., 2020)

SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. - Alejandro Varela-Rial; Maciej Majewski; Alberto Cuzzolin; Gerard Martínez-Rosell; Gianni De Fabritiis (J. Chem. Inf. Model., 2020)

Guided Exploration with Proximal Policy Optimization using a Single Demonstration. - Gabriele Libardi; Gianni De Fabritiis; Sebastian Dittert (ICML, 2021)

Equivariant Transformers for Neural Network based Molecular Potentials. - Philipp Thölke; Gianni De Fabritiis (ICLR, 2022)

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (CoRR, 2022)

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials. - Philipp Thölke; Gianni De Fabritiis (CoRR, 2022)

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. - 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 (CoRR, 2022)

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. - Maciej Majewski; Adrià Pérez; Philipp Thölke; Stefan Doerr; Nicholas E. Charron; Toni Giorgino; Brooke E. Husic; Cecilia Clementi; Frank Noé; Gianni De Fabritiis (CoRR, 2022)

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. - Alejandro Varela-Rial; Iain Maryanow; Maciej Majewski; Stefan Doerr; Nikolai Schapin; José Jiménez-Luna; Gianni De Fabritiis (J. Chem. Inf. Model., 2022)

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials. - Guillem Simeon; Gianni De Fabritiis (NeurIPS, 2023)

Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics. - Pablo Herrera-Nieto; Adrià Pérez; Gianni De Fabritiis (CoRR, 2023)

Top-down machine learning of coarse-grained protein force-fields. - Carles Navarro; Maciej Majewski; Gianni De Fabritiis (CoRR, 2023)

Machine Learning Small Molecule Properties in Drug Discovery. - Nikolai Schapin; Maciej Majewski; Alejandro Varela-Rial; Carlos Arroniz; Gianni De Fabritiis (CoRR, 2023)

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. - 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 004; Andreas Krämer; Julien Michel; et al. (CoRR, 2023)

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series. - Francesc Sabanés Zariquiey; Adrià Pérez; Maciej Majewski; Emilio Gallicchio; Gianni De Fabritiis (J. Chem. Inf. Model., 2023)

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (J. Chem. Inf. Model., 2023)

TorchRL: A data-driven decision-making library for PyTorch. - Albert Bou; Matteo Bettini; Sebastian Dittert; Vikash Kumar; Shagun Sodhani; Xiaomeng Yang; Gianni De Fabritiis; Vincent Moens (ICLR, 2024)

BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO. - Sebastian Dittert; Vincent Moens; Gianni De Fabritiis (NeurIPS, 2024)

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. - Raúl P. Peláez; Guillem Simeon; Raimondas Galvelis; Antonio Mirarchi; Peter K. Eastman; Stefan Doerr; Philipp Thölke; Thomas E. Markland; Gianni De Fabritiis (CoRR, 2024)

On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials. - Guillem Simeon; Antonio Mirarchi; Raúl P. Peláez; Raimondas Galvelis; Gianni De Fabritiis (CoRR, 2024)

PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks. - Nikolai Schapin; Maciej Majewski; Mariona Torrens-Fontanals; Gianni De Fabritiis (CoRR, 2024)

On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction. - Nikolai Schapin; Carles Navarro; Albert Bou; Gianni De Fabritiis (CoRR, 2024)

Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations. - Gianni De Fabritiis (CoRR, 2024)

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics. - Antonio Mirarchi; Raúl P. Peláez; Guillem Simeon; Gianni De Fabritiis (CoRR, 2024)

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery. - Albert Bou; Morgan Thomas; Sebastian Dittert; Carles Navarro; Maciej Majewski; Ye Wang 024; Shivam Patel; Gary Tresadern; Mazen Ahmad; Vincent Moens; Woody Sherman; Simone Sciabola; et al. (J. Chem. Inf. Model., 2024)

PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data. - Mariona Torrens-Fontanals; Panagiotis Tourlas; Stefan Doerr; Gianni De Fabritiis (J. Chem. Inf. Model., 2024)

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. - Francesc Sabanés Zariquiey; Raimondas Galvelis; Emilio Gallicchio; John D. Chodera; Thomas E. Markland; Gianni De Fabritiis (J. Chem. Inf. Model., 2024)

PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. - Morgan Thomas; Mazen Ahmad; Gary Tresadern; Gianni De Fabritiis (J. Cheminformatics, 2024)

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials. - Francesc Sabanés Zariquiey; Stephen E. Farr; Stefan Doerr; Gianni De Fabritiis (J. Chem. Inf. Model., 2025)

REINFORCE-ING Chemical Language Models in Drug Design. - Morgan Thomas; Albert Bou; Gianni De Fabritiis (CoRR, 2025)

Test-Time Training Scaling for Chemical Exploration in Drug Design. - Morgan Thomas; Albert Bou; Gianni De Fabritiis (CoRR, 2025)