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research & Development

Advancing Our Mission

Our research and development efforts are aimed at advancing our mission to transform drug discovery into a high-accuracy computable task.

We work closely with both public and private institutions, and we frequently publish our findings in reputable academic journals. In addition, we have initiated and continue to support various open access science and open source software projects.

We are always open to considering new collaborations.
Contact us
Acellera ACEMD preview code
Open source projects

Molecular Modelling For Everyone

We lead and contribute to a number of open source molecular modeling and manipulation projects such as HTMD, MoleculeKit, OpenMM, ACEGEN, TorchMD and TorchMD-Net.
Open access

PlayMolecule.org – An Open Molecular Discovery Toolkit

PlayMolecule features a number of hosted apps for molecular discovery including a powerful online molecular viewer, molecular preparation tools, and binding affinty affinity prediction tools.
ACEMD, OPENMM and torchMD

Leaders in Molecular Dynamics

First launched in 2008, the ACEMD molecular simulation engine was the first biomolecular code to run on GPUs. Since 2020, ACEMD and OpenMM joined forces to innovate and deliver the most advanced molecular dynamics tools. We also contributed to TorchMD, a deep learning framework for molecular simulations.

Publications

>11,000
Papers citing Acellera's founders
>900
Papers citing ACEMD software
18
Years of know-how in computational drug discovery
Today, our software ACEMD has over 939 citations, and KDeep and Deepsite are among the most cited papers in the field of ML applied to drug discovery.
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 results of
0
publications.

Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials

Sabanés Zariquiey, Francesc; Galvelis, Raimondas; Gallicchio, Emilio; Chodera, John D; Markland, Thomas E; De Fabritiis, Gianni;

Journal of Chemical Information and Modeling
, 
2024
Applications
https://doi.org/10.1021/acs.jcim.3c02031

ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

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

arXiv preprint arXiv:2405.04657
, 
2024
Applications
https://doi.org/10.48550/arXiv.2405.0465

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

Pelaez, Raul P; Simeon, Guillem; Galvelis, Raimondas; Mirarchi, Antonio; Eastman, Peter; Doerr, Stefan; Thölke, Philipp; Markland, Thomas E; De Fabritiis, Gianni;

Journal of Chemical Theory and Computation
, 
2024
Applications
https://doi.org/10.1021/acs.jctc.4c00253

PlayMolecule Viewer: a toolkit for the visualization of molecules and other data

Torrens-Fontanals, Mariona; Tourlas, Panagiotis; Doerr, Stefan; De Fabritiis, Gianni;

Journal of Chemical Information and Modeling
, 
2024
Applications
https://doi.org/10.1021/acs.jcim.3c01776

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

Bou, Albert; Bettini, Matteo; Dittert, Sebastian; Kumar, Vikash; Sodhani, Shagun; Yang, Xiaomeng; De Fabritiis, Gianni; Moens, Vincent;

ICLR 2024, arXiv preprint arXiv:2306.00577
, 
2023
Applications
https://doi.org/10.48550/arXiv.2306.00577

Machine Learning Small Molecule Properties in Drug Discovery

Schapin, Nikolai; Majewski, Maciej; Varela-Rial, Alejandro; Arroniz, Carlos; De Fabritiis, Gianni;

Artificial Intelligence Chemistry
, 
2023
Molecular Simulations
https://doi.org/10.1016/j.aichem.2023.100020

Machine learning coarse-grained potentials of protein thermodynamics

Majewski, Maciej; Pérez, Adrià; Thölke, Philipp; Doerr, Stefan; Charron, Nicholas E; Giorgino, Toni; Husic, Brooke E; Clementi, Cecilia; Noé, Frank; De Fabritiis, Gianni;

Nature Communications
, 
2023
Molecular Simulations
https://doi.org/10.1038/s41467-023-41343-1

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

Sabanés Zariquiey, Francesc; Pérez, Adrià; Majewski, Maciej; Gallicchio, Emilio; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2023
Applications
https://doi.org/10.1021/acs.jcim.3c00178

Top-Down Machine Learning of Coarse-Grained Protein Force Fields

Navarro, Carles; Majewski, Maciej; De Fabritiis, Gianni;

Journal of Chemical Theory and Computation
, 
2023
Molecular Simulations
https://doi.org/10.1021%2Facs.jctc.3c00638

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

Galvelis, Raimondas; Varela-Rial, Alejandro; Doerr, Stefan; Fino, Roberto; Eastman, Peter; Markland, Thomas E; Chodera, John D; De Fabritiis, Gianni;

Journal of chemical information and modeling
, 
2023
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/acs.jcim.3c00773

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

Sabanés Zariquiey, Francesc; Pérez, Adrià; Majewski, Maciej; Gallicchio, Emilio; De Fabritiis, Gianni;

Journal of chemical information and modeling
, 
2023
Applications
https://doi.org/10.1021/acs.jcim.3c00178

Openmm 8: Molecular dynamics simulation with machine learning potentials

Eastman, Peter; Galvelis, Raimondas; Peláez, Raúl P; Abreu, Charlles RA; Farr, Stephen E; Gallicchio, Emilio; Gorenko, Anton; Henry, Michael M; Hu, Frank; Huang, Jing;

The Journal of Physical Chemistry B
, 
2023
Molecular Simulations
https://doi.org/10.1021/acs.jpcb.3c06662

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

Thölke, Philipp; De Fabritiis, Gianni

International Conference on Learning Representations
, 
2022
Applications
https://doi.org/10.48550/arXiv.2202.02541

PlayMolecule glimpse: Understanding protein–ligand property predictions with interpretable neural networks

Varela-Rial, Alejandro; Maryanow, Iain; Majewski, Maciej; Doerr, Stefan; Schapin, Nikolai; Jiménez-Luna, José; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2022
Applications
https://doi.org/10.1021/acs.jcim.1c00691

Structure based virtual screening: Fast and slow

Varela‐Rial, Alejandro; Majewski, Maciej; De Fabritiis, Gianni

Wiley Interdisciplinary Reviews: Computational Molecular Science
, 
2022
Protein-Ligand Binding
https://doi.org/10.1002/wcms.1544

TorchMD: A deep learning framework for molecular simulations

Doerr, Stefan; Majewski, Maciej; Pérez, Adrià; Kramer, Andreas; Clementi, Cecilia; Noe, Frank; Giorgino, Toni; De Fabritiis, Gianni

Journal of chemical theory and computation
, 
2021
Applications
https://doi.org/10.1021/acs.jctc.0c01343

PlayMolecule CrypticScout: predicting protein cryptic sites using mixed-solvent molecular simulations

Martinez-Rosell, Gerard; Lovera, Silvia; Sands, Zara A; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2020
Applications
https://doi.org/10.1021/acs.jcim.9b01209

SkeleDock: a web application for scaffold docking in PlayMolecule

Varela-Rial, Alejandro; Majewski, Maciej; Cuzzolin, Alberto; Martínez-Rosell, Gerard; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2020
Applications
https://doi.org/10.1021/acs.jcim.0c00143

Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations

Herrera-Nieto, Pablo; Pérez, Adrià; De Fabritiis, Gianni

Scientific reports
, 
2020
Applications
https://doi.org/10.1038/s41598-020-69322-2

Small molecule modulation of intrinsically disordered proteins using molecular dynamics simulations

Herrera-Nieto, Pablo; Pérez, Adrià; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2020
Applications
https://doi.org/10.1021/acs.jcim.0c00381

AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations

Adrià Pérez, Pablo Herrera-Nieto, Stefan Doerr, and Gianni De Fabritiis

J. Chem. Theory Comput.
, 
2020
Applications
https://doi.org/10.1021/acs.jctc.0c00205

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

Galvelis, Raimondas; Doerr, Stefan; Damas, João M; Harvey, Matt; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2019
Applications
https://doi.org/10.1021/acs.jcim.9b00439

From target to drug: generative modeling for the multimodal structure-based ligand design

Skalic, Miha; Sabbadin, Davide; Sattarov, Boris; Sciabola, Simone; De Fabritiis, Gianni

Molecular pharmaceutics
, 
2019
Applications
https://doi.org/10.1021/acs.molpharmaceut.9b00634

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

Skalic, Miha; Varela-Rial, Alejandro; Jiménez, José; Martínez-Rosell, Gerard; De Fabritiis, Gianni

Bioinformatics
, 
2019
Applications
https://doi.org/10.1093/bioinformatics/bty583

DeltaDelta neural networks for lead optimization of small molecule potency

Jiménez-Luna, José; Pérez-Benito, Laura; Martinez-Rosell, Gerard; Sciabola, Simone; Torella, Rubben; Tresadern, Gary; De Fabritiis, Gianni

Chemical science
, 
2019
Applications
https://doi.org/10.1039/C9SC04606B

Reconstruction of apo A2A receptor activation pathways reveal ligand-competent intermediates and state-dependent cholesterol hotspots

Lovera, Silvia; Cuzzolin, Alberto; Kelm, Sebastian; De Fabritiis, Gianni; Sands, Zara A

Scientific Reports
, 
2019
Applications
https://doi.org/10.1038/s41598-019-50752-6

Shape-based generative modeling for de novo drug design

Skalic, Miha; Jiménez, José; Sabbadin, Davide; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2019
Applications
https://doi.org/10.1021/acs.jcim.8b00706

K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks

Jiménez, José; Skalic, Miha; Martinez-Rosell, Gerard; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2018
Applications
https://doi.org/10.1021/acs.jcim.7b00650

Molecular-simulation-driven fragment screening for the discovery of new CXCL12 inhibitors

Martinez-Rosell, Gerard; Harvey, Matt J; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2018
Fragment Based Drug Discovery
https://doi.org/10.1021/acs.jcim.7b00625

Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs

Ferruz, Noelia; Doerr, Stefan; Vanase-Frawley, Michelle A; Zou, Yaozhong; Chen, Xiaomin; Marr, Eric S; Nelson, Robin T; Kormos, Bethany L; Wager, Travis T; Hou, Xinjun; Villalobos, Anabella; Sciabola, Simone; De Fabritiis, Gianni

Scientific reports
, 
2018
Protein-Ligand Binding
https://doi.org/10.1038/s41598-018-19345-7

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

Skalic, Miha; Martínez-Rosell, Gerard; Jiménez, José; De Fabritiis, Gianni

Bioinformatics
, 
2018
Applications
https://doi.org/10.1093/bioinformatics/bty758

PathwayMap: molecular pathway association with self-normalizing neural networks

Jimenez, Jose; Sabbadin, Davide; Cuzzolin, Alberto; Martinez-Rosell, Gerard; Gora, Jacob; Manchester, John; Duca, Jose; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2018
Applications
https://doi.org/10.1021/acs.jcim.8b00711

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

Plattner, Nuria; Doerr, Stefan; De Fabritiis, Gianni; Noé, Frank

Nature chemistry
, 
2017
Conformational Studies
https://doi.org/10.1038/nchem.2785

High-throughput automated preparation and simulation of membrane proteins with HTMD

Doerr, Stefan; Giorgino, Toni; Martínez-Rosell, Gerard; Damas, Joao M; De Fabritiis, Gianni

Journal of Chemical Theory and Computation
, 
2017
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/acs.jctc.7b00480

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

Jiménez, José; Doerr, Stefan; Martínez-Rosell, Gerard; Rose, Alexander S; De Fabritiis, Gianni

Bioinformatics
, 
2017
Applications
https://doi.org/10.1093/bioinformatics/btx350

Dimensionality reduction methods for molecular simulations

Doerr, Stefan; Ariz-Extreme, Igor; Harvey, Matthew J; De Fabritiis, Gianni

arXiv preprint arXiv:1710.10629
, 
2017
Molecular Simulations
https://doi.org/10.48550/arXiv.1710.10629

Optimizing Proteins and Ligands for Computerized Drug Discovery

Damas, João; Cuzzolin, Alberto; Galvelis, Raimondas; Doerr, Stefan; Martínez-Rosell, Gerard; Harvey, Matt; De Fabritiis, Gianni

, 
2017
Applications
https://doi.org/10.3390/mol2net-03-05072

PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations

Martínez-Rosell, Gerard; Giorgino, Toni; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2017
Applications
https://doi.org/10.1021/acs.jcim.7b00190

Drug discovery and molecular dynamics: methods, applications and perspective beyond the second timescale

Martinez-Rosell, Gerard; Giorgino, Toni; Harvey, Matt J; de Fabritiis, Gianni

Current topics in medicinal chemistry
, 
2017
Applications
https://doi.org/10.2174/1568026617666170418121951

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

Stanley, Nathaniel; Pardo, Leonardo; De Fabritiis, Gianni

Scientific reports
, 
2016
Membrane Proteins
https://doi.org/10.1038/srep22639

HTMD: high-throughput molecular dynamics for molecular discovery

Doerr, S; Harvey, MJ; Noé, Frank; De Fabritiis, G

Journal of chemical theory and computation
, 
2016
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/acs.jctc.6b00049

Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme

Ferruz, Noelia; Tresadern, Gary; Pineda-Lucena, Antonio; De Fabritiis, Gianni

Scientific reports
, 
2016
Protein-Ligand Binding
https://doi.org/10.1038/srep30275

Binding kinetics in drug discovery

Ferruz, Noelia; De Fabritiis, Gianni

Molecular Informatics
, 
2016
Protein-Ligand Binding
https://doi.org/10.1002/minf.201501018

HTMD: A complete software workspace for simulation-guided drug design

Doerr, Stefan; Harvey, Matt; De Fabritiis, Gianni

ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
, 
2015
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/acs.jctc.6b00049

Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation

Martí-Solano, Maria; Iglesias, Alba; de Fabritiis, Gianni; Sanz, Ferran; Brea, José; Loza, M Isabel; Pastor, Manuel; Selent, Jana

Molecular pharmacology
, 
2015
Fragment Based Drug Discovery
https://doi.org/10.1124/mol.114.097022

AceCloud: molecular dynamics simulations in the cloud

Harvey, Matt J; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2015
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/acs.jcim.5b00086

Insights from fragment hit binding assays by molecular simulations

Ferruz, Noelia; Harvey, Matthew J; Mestres, Jordi; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2015
Fragment Based Drug Discovery
https://doi.org/10.1021/acs.jcim.5b00453

Reranking docking poses using molecular simulations and approximate free energy methods

Lauro, G; Ferruz, Noelia; Fulle, Simone; Harvey, Matt J; Finn, Paul W; De Fabritiis, Gianni

Journal of chemical information and modeling
, 
2014
Molecular Simulations
https://doi.org/10.1021/ci500309a

On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations

Doerr, S; De Fabritiis, G

Journal of chemical theory and computation
, 
2014
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/ct400919u

Membrane lipids are key modulators of the endocannabinoid-hydrolase FAAH

Dainese, Enrico; De Fabritiis, Gianni; Sabatucci, Annalaura; Oddi, Sergio; Angelucci, Clotilde Beatrice; Di Pancrazio, Chiara; Giorgino, Toni; Stanley, Nathaniel; Del Carlo, Michele; Cravatt, Benjamin F

Biochemical Journal
, 
2014
Applications
https://doi.org/10.1042/BJ20130960

Kinetic characterization of fragment binding in AmpC β-lactamase by high-throughput molecular simulations

Bisignano, Paola; Doerr, Stefan; Harvey, Matt J; Favia, Angelo D; Cavalli, Andrea; De Fabritiis, Gianni

Journal of Chemical Information and Modeling
, 
2014
Conformational Studies
https://doi.org/10.1021/ci4006063

Kinetic modulation of a disordered protein domain by phosphorylation

Stanley, Nathaniel; Esteban-Martín, Santiago; De Fabritiis, Gianni

Nature communications
, 
2014
Conformational Studies
https://doi.org/10.1038/ncomms6272

Identification of slow molecular order parameters for Markov model construction

Pérez-Hernández, Guillermo; Paul, Fabian; Giorgino, Toni; De Fabritiis, Gianni; Noé, Frank

The Journal of chemical physics
, 
2013
ACEMD/HTMD/AceCloud
https://doi.org/10.1063/1.4811489

Visualizing the induced binding of SH2-phosphopeptide

Giorgino, T; Buch, I; De Fabritiis, G

Journal of chemical theory and computation
, 
2012
Protein-Ligand Binding
https://doi.org/10.1021/ct300003f

Thumbs down for HIV: domain level rearrangements do occur in the NNRTI-bound HIV-1 reverse transcriptase

Wright, David W; Sadiq, S Kashif; De Fabritiis, Gianni; Coveney, Peter V

Journal of the American Chemical Society
, 
2012
Conformational Studies
https://doi.org/10.1021/ja301565k

High-throughput molecular dynamics: the powerful new tool for drug discovery

Harvey, Matthew J; De Fabritiis, Gianni

Drug discovery today
, 
2012
Molecular Simulations
https://doi.org/10.1016/j.drudis.2012.03.017

Kinetic characterization of the critical step in HIV-1 protease maturation

Sadiq, S Kashif; Noé, Frank; De Fabritiis, Gianni

Proceedings of the National Academy of Sciences
, 
2012
Conformational Studies
https://doi.org/10.1073/pnas.1211457109

Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations

Buch, Ignasi; Giorgino, Toni; De Fabritiis, Gianni

Proceedings of the National Academy of Sciences
, 
2011
Protein-Ligand Binding
https://doi.org/10.1073/pnas.1103547108

A high-throughput steered molecular dynamics study on the free energy profile of ion permeation through gramicidin A

Giorgino, Toni; De Fabritiis, Gianni

Journal of Chemical Theory and Computation
, 
2011
Applications
https://doi.org/10.1021/ct100707s

Optimized potential of mean force calculations for standard binding free energies

Buch, Ignasi; Sadiq, S Kashif; De Fabritiis, Gianni

Journal of Chemical Theory and Computation
, 
2011
Applications
https://doi.org/10.1021/ct2000638

High-throughput all-atom molecular dynamics simulations using distributed computing

Buch, I; Harvey, Matt J; Giorgino, T; Anderson, DP; De Fabritiis, G

Journal of chemical information and modeling
, 
2010
Applications
https://doi.org/10.1021/ci900455r

Induced effects of sodium ions on dopaminergic G-protein coupled receptors

Selent, Jana; Sanz, Ferran; Pastor, Manuel; De Fabritiis, Gianni

PLoS Computational Biology
, 
2010
Membrane Proteins
https://doi.org/10.1371/journal.pcbi.1000884

Explicit solvent dynamics and energetics of HIV‐1 protease flap opening and closing

Sadiq, S Kashif; De Fabritiis, Gianni

Proteins: Structure, Function, and Bioinformatics
, 
2010
Conformational Studies
https://doi.org/10.1002/prot.22806

An implementation of the smooth particle mesh Ewald method on GPU hardware

Harvey, MJ; De Fabritiis, G

Journal of Chemical Theory and Computation
, 
2009
Conformational Studies
https://doi.org/10.1021/ct900275y

ACEMD: Accelerating biomolecular dynamics in the microsecond time scale

Harvey, MJ; Giupponi, G; De Fabritiis, G

Journal of Chemical Theory and Computation
, 
2009
ACEMD/HTMD/AceCloud
https://doi.org/10.1021/ct9000685

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

Giupponi, G; Harvey, MJ; De Fabritiis, G

Drug discovery today
, 
2008
Molecular Simulations
https://doi.org/10.1016/j.drudis.2008.08.001
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International And EU Funded Projects
A cloud application platform for rational drug discovery using high throughput molecular dynamics.
European SME innovation Associate(H2020-INNOSUP-02-2016, Grant Agreement 739649)
Computer-Centric Fragment Based Ligand Discovery for the Development of candidate molecules targeting the chemkine system.
Nuclis d’Innovació Tecnològica 2014. Acció, Generalitat de Catalunya. Nuclis Transnacionals Programa Bilateral Catalunya-Israel, Project nr. RDIS14-1-0002. 2014-2016
Feasibility assessment of a cloud application platform for rational drug design using high-throughput.
H2020 SME Instrument 2014, Grant Agreement nr. 674659. 2015
A Centre of Excellence in Computational Biomedicine.
H2020-EINFRA-2015-1, Grant Agreement nr. 675451. 2016-2019
A Centre of Excellence in Computational Biomedicine.
H2020-INFRAEDI-02-2018, Grant Agreement nr. 823712. 2019-2023