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Zinovjev K, Hedges L, Montagud Andreu R, Woods C, Tuñón I, van der Kamp MW. emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations. J Chem Theory Comput 2024; 20:4514-4522. [PMID: 38804055 PMCID: PMC11171281 DOI: 10.1021/acs.jctc.4c00248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Abstract
We present in this work the emle-engine package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid machine learning potential/molecular-mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tunable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced sampling molecular dynamics simulations. Through the calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling the usage of EMLE embedding in practical ML/MM simulations, emle-engine will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment.
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Affiliation(s)
- Kirill Zinovjev
- Departamento
de Química Física, Universidad
de Valencia, 46100 Burjassot, Spain
| | - Lester Hedges
- School
of Biochemistry, University of Bristol, Biomedical Sciences Building, University
Walk, Bristol BS8 1TD, U.K.
- Research
Software Engineering, Advanced Computing
Research Centre, 31 Great
George Street, Bristol BS1 5QD, U.K.
| | | | - Christopher Woods
- Research
Software Engineering, Advanced Computing
Research Centre, 31 Great
George Street, Bristol BS1 5QD, U.K.
| | - Iñaki Tuñón
- Departamento
de Química Física, Universidad
de Valencia, 46100 Burjassot, Spain
| | - Marc W. van der Kamp
- School
of Biochemistry, University of Bristol, Biomedical Sciences Building, University
Walk, Bristol BS8 1TD, U.K.
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2
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Rossetti G, Mandelli D. How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms. Curr Opin Struct Biol 2024; 86:102814. [PMID: 38631106 DOI: 10.1016/j.sbi.2024.102814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024]
Abstract
Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking the potential of more rigorous quantum mechanical/molecular mechanics (QM/MM) models combined with molecular dynamics-based free energy techniques could have a tremendous impact. Indeed, these two relatively old techniques are emerging as promising methods in the field. This has been favored by the exponential growth of computer power and the proliferation of powerful data-driven methods. Here, we briefly review recent advances and applications, and give our perspective on the impact that QM/MM and free-energy methods combined with machine learning-aided algorithms can have on drug design.
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Affiliation(s)
- Giulia Rossetti
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany; Department of Neurology, University Hospital Aachen (UKA), RWTH Aachen University, Aachen, Germany; Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Jülich 52428, Germany. https://twitter.com/G_Rossetti_
| | - Davide Mandelli
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany.
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3
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Carrasco-Busturia D, Ippoliti E, Meloni S, Rothlisberger U, Olsen JMH. Multiscale biomolecular simulations in the exascale era. Curr Opin Struct Biol 2024; 86:102821. [PMID: 38688076 DOI: 10.1016/j.sbi.2024.102821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024]
Abstract
The complexity of biological systems and processes, spanning molecular to macroscopic scales, necessitates the use of multiscale simulations to get a comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations are crucial for capturing processes beyond the reach of classical MD simulations. The advent of exascale computing offers unprecedented opportunities for scientific exploration, not least within life sciences, where simulations are essential to unravel intricate molecular mechanisms underlying biological processes. However, leveraging the immense computational power of exascale computing requires innovative algorithms and software designs. In this context, we discuss the current status and future prospects of multiscale biomolecular simulations on exascale supercomputers with a focus on QM/MM MD. We highlight our own efforts in developing a versatile and high-performance multiscale simulation framework with the aim of efficient utilization of state-of-the-art supercomputers. We showcase its application in uncovering complex biological mechanisms and its potential for leveraging exascale computing.
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Affiliation(s)
- David Carrasco-Busturia
- DTU Chemistry, Technical University of Denmark (DTU), Kongens Lyngby, DK-2800, Denmark. https://twitter.com/@DavidCdeB
| | - Emiliano Ippoliti
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, DE-52428, Germany
| | - Simone Meloni
- Dipartimento di Scienze Chimiche, Farmaceutiche ed Agrarie (DOCPAS), Università degli Studi di Ferrara (Unife), Ferrara, I-44121, Italy. https://twitter.com/@smeloni99
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland. https://twitter.com/@lcbc_epfl
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4
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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5
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Wang Y, Inizan TJ, Liu C, Piquemal JP, Ren P. Incorporating Neural Networks into the AMOEBA Polarizable Force Field. J Phys Chem B 2024; 128:2381-2388. [PMID: 38445577 PMCID: PMC10985787 DOI: 10.1021/acs.jpcb.3c08166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations.
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Affiliation(s)
- Yanxing Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Théo Jaffrelot Inizan
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Chengwen Liu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J Chem Inf Model 2024; 64:1481-1485. [PMID: 38376463 DOI: 10.1021/acs.jcim.3c02031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Raimondas Galvelis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Emilio Gallicchio
- Department of Chemistry, Graduate Center, Brooklyn College, City University of New York, New York, New York 11210, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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7
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Ding Y, Huang J. Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy. Int J Mol Sci 2024; 25:1448. [PMID: 38338727 PMCID: PMC10855459 DOI: 10.3390/ijms25031448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.
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Affiliation(s)
- Ye Ding
- College of Life Sciences, Zhejiang University, Hangzhou 310027, China;
- School of Life Sciences, Westlake University, Hangzhou 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
| | - Jing Huang
- School of Life Sciences, Westlake University, Hangzhou 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
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Ding Y, Huang J. DP/MM: A Hybrid Model for Zinc-Protein Interactions in Molecular Dynamics. J Phys Chem Lett 2024; 15:616-627. [PMID: 38198685 DOI: 10.1021/acs.jpclett.3c03158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Zinc-containing proteins are vital for many biological processes, yet accurately modeling them using classical force fields is hindered by complicated polarization and charge transfer effects. This study introduces DP/MM, a hybrid force field scheme that utilizes a deep potential model to correct the atomic forces of zinc ions and their coordinated atoms, elevating them from MM to QM levels of accuracy. Trained on the difference between MM and QM atomic forces across diverse zinc coordination groups, the DP/MM model faithfully reproduces structural characteristics of zinc coordination during simulations, such as the tetrahedral coordination of Cys4 and Cys3His1 groups. Furthermore, DP/MM allows water exchange in the zinc coordination environment. With its unique blend of accuracy, efficiency, flexibility, and transferability, DP/MM serves as a valuable tool for studying structures and dynamics of zinc-containing proteins and also represents a pioneering approach in the evolving landscape of machine learning potentials for molecular modeling.
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Affiliation(s)
- Ye Ding
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310027, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
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9
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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J Phys Chem B 2024; 128:109-116. [PMID: 38154096 PMCID: PMC10846090 DOI: 10.1021/acs.jpcb.3c06662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Raimondas Galvelis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Raúl P. Peláez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Charlles R. A. Abreu
- Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil
- Redesign Science Inc., 180 Varick St., New York, NY 10014, USA
| | - Stephen E. Farr
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, NY, USA
- Ph.D. Program in Chemistry and Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, USA
| | - Anton Gorenko
- Stream HPC, Koningin Wilhelminaplein 1 - 40601, 1062 HG Amsterdam, Netherlands
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Frank Hu
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Joshua A. Mitchell
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA 95616, USA
| | - Vijay S. Pande
- Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, CA 94025, USA
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - João PGLM Rodrigues
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - Jaime Rodriguez-Guerra
- Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany
| | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Jason Swails
- Entos Inc., 9310 Athena Circle, La Jolla, CA 92037, USA
| | - Philip Turner
- College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, NY 10004, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
- ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
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