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Science and Innovation

Since 2006, Acellera supports applied research for the development of biomedical simulation methods and machine learning predictors via agreements with academic institutions. Furthermore, we create new software and hardware infrastructure for performing biomolecular simulations in high-throughput. Salient examples are ACEMD, HTMD or Metrocubo.

We would be happy to consider scientific collaborations with interested parties to demonstrate the potential of biomolecular simulations and deep learning. We are open to research projects (e.g. European projects) as well as other collaboration formats. Do not hesitate to contact us!

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

Publications

  • Varela-Rial, A., Maryanow, I., Majewski, M., Doerr, S., Schapin, N., Jiménez-Luna, J., & De Fabritiis, G. (2022). PlayMolecule glimpse: Understanding protein–ligand property predictions with interpretable neural networks. Journal of Chemical Information and Modeling, 62(2), 225–231. https://doi.org/10.1021/acs.jcim.1c00691
  • Doerr, S., Majewski, M., Pérez, A., Kramer, A., Clementi, C., Noé, F., … & De Fabritiis, G. (2021). Torchmd: A deep learning framework for molecular simulations. Journal of Chemical Theory and Computation, 17(4), 2355-2363. https://doi.org/10.1021/acs.jctc.0c01343
  • Pérez, A., Herrera-Nieto, P., Doerr, S., & De Fabritiis, G. (2020). AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations. Journal of Chemical Theory and Computation, 16(7), 4685-4693. https://doi.org/10.1021/acs.jctc.0c00205
  • Varela-Rial, A., Majewski, M., Cuzzolin, A., Martínez-Rosell, G., & De Fabritiis, G. (2020). SkeleDock: A web application for scaffold docking in PlayMolecule. Journal of Chemical Information and Modeling, 60(7), 3639–3643. https://doi.org/10.1021/acs.jcim.0c00143
  • Herrera-Nieto, P., Pérez, A., & De Fabritiis, G. (2020). Small molecule modulation of intrinsically disordered proteins using molecular dynamics simulations. Journal of Chemical Information and Modeling, 60(10), 5003-5010. https://doi.org/10.1021/acs.jcim.0c00381
  • Martinez-Rosell, G., Lovera, S., Sands, Z. A., & De Fabritiis, G. (2020). PlayMolecule CrypticScout: Predicting protein cryptic sites using mixed-solvent molecular simulations. Journal of Chemical Information and Modeling, 60(4), 2101–2109. https://doi.org/10.1021/acs.jcim.9b01209
  • Herrera-Nieto, P., Pérez, A., & De Fabritiis, G. (2020). Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations. Scientific reports, 10(1), 1-8. https://doi.org/10.1038/s41598-020-69322-2
  • Lovera, S., Cuzzolin, A., Kelm, S., De Fabritiis, G., & Sands, Z. A. (2019). Reconstruction of apo A2A receptor activation pathways reveal ligand-competent intermediates and state-dependent cholesterol hotspots. Scientific Reports, 9(1), 1-10. https://doi.org/10.1038/s41598-019-50752-6
  • Jiménez-Luna, J., Pérez-Benito, L., Martínez-Rosell, G., Sciabola, S., Torella, R., Tresadern, G., & De Fabritiis, G. (2019). DeltaDelta neural networks for lead optimization of small molecule potency. Chemical Science, 10(47), 10911–10918. https://doi.org/10.1039/C9SC04606B
  • Skalic, M., Jiménez Luna, J., Sabbadin, D., & De Fabritiis, G. (2019). Shape-based generative modeling for de novo drug design. Journal of Chemical Information and Modeling, 59(3), 1188–1195. https://doi.org/10.1021/acs.jcim.8b00706
  • Jiménez, J., Sabbadin, D., Cuzzolin, A., Martínez-Rosell, G., Gora, J., Manchester, J., Duca, J., & De Fabritiis, G. (2018). PathwayMap: Molecular pathway association with self-normalizing neural networks. Journal of Chemical Information and Modeling, 58(12), 2487–2493. https://doi.org/10.1021/acs.jcim.8b00711
  • Skalic, M., Martínez-Rosell, G., Jiménez, J., & De Fabritiis, G. (2018). PlayMolecule BindScope: Large scale CNN-based virtual screening on the web. Bioinformatics, bty758. https://doi.org/10.1093/bioinformatics/bty758
  • Skalic, M., Varela-Rial, A., Jiménez, J., Martínez-Rosell, G., & De Fabritiis, G. (2018). LigVoxel: Inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty583
  • Jiménez Luna, J., Skalic, M., Martinez-Rosell, G., & De Fabritiis, G. (2018). KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. Journal of Chemical Information and Modeling, 58(2), 287-296. https://doi.org/10.1021/acs.jcim.7b00650
  • Jiménez, J., Doerr, S., Martinez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2017). DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Structural Bioinformatics. https://doi.org/10.1093/bioinformatics/btx350
  • Martinez-Rosell, G., Giorgino, T., & de Fabritiis, G. (2017). PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations. Journal of Chemical Information and Modeling, 57(6), 1511-1516. https://doi.org/10.1021/acs.jcim.7b00190
  • Plattner, N., Doerr, S., de Fabritiis, G., & Noé, F. (2017). Complete Protein-Protein Association Kinetics in Atomic Details Revealed by Molecular Dynamics Simulations and Markov Modelling. Nature Chemistry, 9(10), 1005-1011. https://doi.org/10.1038/nchem.2785
  • Martinez-Rosell, G., Giorgino, T., Harvey, M. J., & de Fabritiis, G. (2017). Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale. Current Topics in Medicinal Chemistry, 17(25), 2766-2776. https://doi.org/10.2174/1568026617666170418121951
  • Dainese, E., de Fabritiis, G., Sabatucci, A., Oddi, S., Angelucci, C. B., Di Pancrazio, C., Giorgino, T., Stanley, N., Del Carlo, M., Cravatt, B. F., & Maccarrone, M. (2014). Membrane Lipids Are Key Modulators of the Endocannabinoid-Hydrolase FAAH. Biochemical Journal, 457(3), 463-472. https://doi.org/10.1042/BJ20130960
  • Giorgino, T., & de Fabritiis, G. (2011). A High-Throughput Steered Molecular Dynamics Study on the Free Energy Profile of Ion Permeation through Gramicidin A. Journal of Chemical Theory and Computation, 7(6), 1943-1950. https://doi.org/10.1021/ct100707s
  • Buch, I., Harvey, M. J., Giorgino, T., Anderson, D. P., & de Fabritiis, G. (2010). High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. Journal of Chemical Information and Modeling, 50(3), 397-403. https://doi.org/10.1021/ci900455r
  • Stanley, N., Esteban-Martín, S., & De Fabritiis, G. (2014). Kinetic modulation of a disordered protein domain by phosphorylation. Nature Communications, 5, 5272. https://doi.org/10.1038/ncomms6272
  • Sadiq, S. K., Noé, F., & De Fabritiis, G. (2012). Kinetic characterization of the critical step in HIV-1 protease maturation. Proceedings of the National Academy of Sciences of the United States of America, 109(50), 20449-20454. https://doi.org/10.1073/pnas.1211457109
  • Wright, D. W., Sadiq, S. K., De Fabritiis, G., & Coveney, P. V. (2012). Thumbs Down for HIV: Domain Level Rearrangements Do Occur in the NNRTI-Bound HIV-1 Reverse Transcriptase. Journal of the American Chemical Society, 134(31), 12885-12888. https://doi.org/10.1021/ja301565k
  • Sadiq, K., & De Fabritiis, G. (2010). Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing. Proteins: Structure, Function, and Bioinformatics, 78(13), 2873-2883. https://doi.org/10.1002/prot.22806
  • Varela-Rial, A., Majewski, M., & De Fabritiis, G. (2021). Structure based virtual screening: Fast and slow. Wiley Interdisciplinary Reviews-Computational Molecular Science. Advance online publication. https://doi.org/10.1002/wcms.1544
  • Ferruz, N., Doerr, S., Vanase-Frawley, M. A., Zou, Y., Chen, X., Marr, E. S., Nelson, R. T., Kormos, B. L., Wager, T. T., Hou, X., Villalobos, A., Sciabola, S., & De Fabritiis, G. (2018). Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs. Scientific Reports, 8, 897. https://doi.org/10.1038/s41598-018-19345-7
  • Ferruz, N., Tresadern, G., Pineda-Lucena, A., & De Fabritiis, G. (2016). Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme. Scientific Reports, 6, 30275. https://doi.org/10.1038/srep30275
  • Ferruz, N., & De Fabritiis, G. (2016). Binding kinetics in drug discovery. Molecular Informatics, 35, 392-399. https://doi.org/10.1002/minf.201501018
  • Giorgino, T., Buch, I., & De Fabritiis, G. (2012). Visualizing the induced binding of SH2-phosphopeptide. Journal of Chemical Theory and Computation, 8, 1171-1175. https://doi.org/10.1021/ct300003f
  • Buch, I., Giorgino, T., & De Fabritiis, G. (2011). Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proceedings of the National Academy of Sciences, 108, 10184-10189. https://doi.org/10.1073/pnas.1103547108
  • Buch, I., Sadiq, K., & De Fabritiis, G. (2011). Optimized potential of mean force calculations of standard binding free energy. Journal of Chemical Theory and Computation, 7, 1765-1772. https://doi.org/10.1021/ct2000638
  • Martinez-Rosell, G., Harvey, M. J., & De Fabritiis, G. (2018). Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors. Journal of Chemical Information and Modeling, 58(2), 436-440. https://doi.org/10.1021/acs.jcim.7b00625
  • Ferruz, N., Harvey, M., Mestres, J., & De Fabritiis, G. (2015). Insights from Fragment Hit Binding Assays by Molecular Simulations. Journal of Chemical Information and Modeling, 55(10), 2200-2205. https://doi.org/10.1021/acs.jcim.5b00453
  • Martí-Solano, M., Iglesias, A., De Fabritiis, G., Sanz, F., Brea, J., Loza, M., Pastor, M., & Selent, J. (2015). Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Molecular Pharmacology, 87(4), 740-746. https://doi.org/10.1124/mol.114.097022
  • Stanley, N., Pardo, L., & De Fabritiis, G. (2016). The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor. Scientific Reports, 6, 22639. https://doi.org/10.1038/srep22639
  • Selent, J., Sanz, F., Pastor, M., & De Fabritiis, G. (2010). Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors. PLoS Computational Biology, 6(8), e1000884. https://doi.org/10.1371/journal.pcbi.1000884
  • M. J. Harvey and G. De Fabritiis. (2012). High-throughput molecular dynamics: The powerful new tool for drug discovery. Drug Discovery Today, https://doi.org/10.1016/j.drudis.2012.03.017
  • M. J. Harvey and G. De Fabritiis. (2009). An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware. J. Chem. Theory Comput., 5, 2371–2377. https://doi.org/10.1021/ct900275y
  • G. Giupponi, M. Harvey and G. De Fabritiis. (2008). The impact of accelerator processors for high-throughput molecular modeling and simulation. Drug Discovery Today, 13, 1052. https://doi.org/10.1016/j.drudis.2008.08.001
  • Doerr, S., Harvey, M. J., Noé, F., & De Fabritiis, G. (2016). HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. Journal of Chemical Theory and Computation, 12(4), 1845-1852. https://doi.org/10.1021/acs.jctc.6b00049
  • Harvey, M. J., & De Fabritiis, G. (2015). AceCloud: Molecular Dynamics Simulations in the Cloud. Journal of Chemical Information and Modeling, 55(5), 909-914. https://doi.org/10.1021/acs.jcim.5b00086
  • Doerr, S., & De Fabritiis, G. (2014). On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. Journal of Chemical Theory and Computation, 10(5), 2064-2069. https://doi.org/10.1021/ct400919u
  • Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013). Identification of slow molecular order parameters for Markov model construction. The Journal of Chemical Physics, 139(1), 015102. https://doi.org/10.1063/1.4811489
  • Harvey, M., Giupponi, G., & De Fabritiis, G. (2009). ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale. Journal of Chemical Theory and Computation, 5(6), 1632-1639. https://doi.org/10.1021/ct9000685
Applications
  • Varela-Rial, A., Maryanow, I., Majewski, M., Doerr, S., Schapin, N., Jiménez-Luna, J., & De Fabritiis, G. (2022). PlayMolecule glimpse: Understanding protein–ligand property predictions with interpretable neural networks. Journal of Chemical Information and Modeling, 62(2), 225–231. https://doi.org/10.1021/acs.jcim.1c00691
  • Doerr, S., Majewski, M., Pérez, A., Kramer, A., Clementi, C., Noé, F., … & De Fabritiis, G. (2021). Torchmd: A deep learning framework for molecular simulations. Journal of Chemical Theory and Computation, 17(4), 2355-2363. https://doi.org/10.1021/acs.jctc.0c01343
  • Pérez, A., Herrera-Nieto, P., Doerr, S., & De Fabritiis, G. (2020). AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations. Journal of Chemical Theory and Computation, 16(7), 4685-4693. https://doi.org/10.1021/acs.jctc.0c00205
  • Varela-Rial, A., Majewski, M., Cuzzolin, A., Martínez-Rosell, G., & De Fabritiis, G. (2020). SkeleDock: A web application for scaffold docking in PlayMolecule. Journal of Chemical Information and Modeling, 60(7), 3639–3643. https://doi.org/10.1021/acs.jcim.0c00143
  • Herrera-Nieto, P., Pérez, A., & De Fabritiis, G. (2020). Small molecule modulation of intrinsically disordered proteins using molecular dynamics simulations. Journal of Chemical Information and Modeling, 60(10), 5003-5010. https://doi.org/10.1021/acs.jcim.0c00381
  • Martinez-Rosell, G., Lovera, S., Sands, Z. A., & De Fabritiis, G. (2020). PlayMolecule CrypticScout: Predicting protein cryptic sites using mixed-solvent molecular simulations. Journal of Chemical Information and Modeling, 60(4), 2101–2109. https://doi.org/10.1021/acs.jcim.9b01209
  • Herrera-Nieto, P., Pérez, A., & De Fabritiis, G. (2020). Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations. Scientific reports, 10(1), 1-8. https://doi.org/10.1038/s41598-020-69322-2
  • Lovera, S., Cuzzolin, A., Kelm, S., De Fabritiis, G., & Sands, Z. A. (2019). Reconstruction of apo A2A receptor activation pathways reveal ligand-competent intermediates and state-dependent cholesterol hotspots. Scientific Reports, 9(1), 1-10. https://doi.org/10.1038/s41598-019-50752-6
  • Jiménez-Luna, J., Pérez-Benito, L., Martínez-Rosell, G., Sciabola, S., Torella, R., Tresadern, G., & De Fabritiis, G. (2019). DeltaDelta neural networks for lead optimization of small molecule potency. Chemical Science, 10(47), 10911–10918. https://doi.org/10.1039/C9SC04606B
  • Skalic, M., Jiménez Luna, J., Sabbadin, D., & De Fabritiis, G. (2019). Shape-based generative modeling for de novo drug design. Journal of Chemical Information and Modeling, 59(3), 1188–1195. https://doi.org/10.1021/acs.jcim.8b00706
  • Jiménez, J., Sabbadin, D., Cuzzolin, A., Martínez-Rosell, G., Gora, J., Manchester, J., Duca, J., & De Fabritiis, G. (2018). PathwayMap: Molecular pathway association with self-normalizing neural networks. Journal of Chemical Information and Modeling, 58(12), 2487–2493. https://doi.org/10.1021/acs.jcim.8b00711
  • Skalic, M., Martínez-Rosell, G., Jiménez, J., & De Fabritiis, G. (2018). PlayMolecule BindScope: Large scale CNN-based virtual screening on the web. Bioinformatics, bty758. https://doi.org/10.1093/bioinformatics/bty758
  • Skalic, M., Varela-Rial, A., Jiménez, J., Martínez-Rosell, G., & De Fabritiis, G. (2018). LigVoxel: Inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty583
  • Jiménez Luna, J., Skalic, M., Martinez-Rosell, G., & De Fabritiis, G. (2018). KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. Journal of Chemical Information and Modeling, 58(2), 287-296. https://doi.org/10.1021/acs.jcim.7b00650
  • Jiménez, J., Doerr, S., Martinez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2017). DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Structural Bioinformatics. https://doi.org/10.1093/bioinformatics/btx350
  • Martinez-Rosell, G., Giorgino, T., & de Fabritiis, G. (2017). PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations. Journal of Chemical Information and Modeling, 57(6), 1511-1516. https://doi.org/10.1021/acs.jcim.7b00190
  • Plattner, N., Doerr, S., de Fabritiis, G., & Noé, F. (2017). Complete Protein-Protein Association Kinetics in Atomic Details Revealed by Molecular Dynamics Simulations and Markov Modelling. Nature Chemistry, 9(10), 1005-1011. https://doi.org/10.1038/nchem.2785
  • Martinez-Rosell, G., Giorgino, T., Harvey, M. J., & de Fabritiis, G. (2017). Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale. Current Topics in Medicinal Chemistry, 17(25), 2766-2776. https://doi.org/10.2174/1568026617666170418121951
  • Dainese, E., de Fabritiis, G., Sabatucci, A., Oddi, S., Angelucci, C. B., Di Pancrazio, C., Giorgino, T., Stanley, N., Del Carlo, M., Cravatt, B. F., & Maccarrone, M. (2014). Membrane Lipids Are Key Modulators of the Endocannabinoid-Hydrolase FAAH. Biochemical Journal, 457(3), 463-472. https://doi.org/10.1042/BJ20130960
  • Giorgino, T., & de Fabritiis, G. (2011). A High-Throughput Steered Molecular Dynamics Study on the Free Energy Profile of Ion Permeation through Gramicidin A. Journal of Chemical Theory and Computation, 7(6), 1943-1950. https://doi.org/10.1021/ct100707s
  • Buch, I., Harvey, M. J., Giorgino, T., Anderson, D. P., & de Fabritiis, G. (2010). High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. Journal of Chemical Information and Modeling, 50(3), 397-403. https://doi.org/10.1021/ci900455r

 

Conformational Studies
  • Stanley, N., Esteban-Martín, S., & De Fabritiis, G. (2014). Kinetic modulation of a disordered protein domain by phosphorylation. Nature Communications, 5, 5272. https://doi.org/10.1038/ncomms6272
  • Sadiq, S. K., Noé, F., & De Fabritiis, G. (2012). Kinetic characterization of the critical step in HIV-1 protease maturation. Proceedings of the National Academy of Sciences of the United States of America, 109(50), 20449-20454. https://doi.org/10.1073/pnas.1211457109
  • Wright, D. W., Sadiq, S. K., De Fabritiis, G., & Coveney, P. V. (2012). Thumbs Down for HIV: Domain Level Rearrangements Do Occur in the NNRTI-Bound HIV-1 Reverse Transcriptase. Journal of the American Chemical Society, 134(31), 12885-12888. https://doi.org/10.1021/ja301565k
  • Sadiq, K., & De Fabritiis, G. (2010). Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing. Proteins: Structure, Function, and Bioinformatics, 78(13), 2873-2883. https://doi.org/10.1002/prot.22806

 

Protein-Ligand Binding
  • Varela-Rial, A., Majewski, M., & De Fabritiis, G. (2021). Structure based virtual screening: Fast and slow. Wiley Interdisciplinary Reviews-Computational Molecular Science. Advance online publication. https://doi.org/10.1002/wcms.1544
  • Ferruz, N., Doerr, S., Vanase-Frawley, M. A., Zou, Y., Chen, X., Marr, E. S., Nelson, R. T., Kormos, B. L., Wager, T. T., Hou, X., Villalobos, A., Sciabola, S., & De Fabritiis, G. (2018). Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs. Scientific Reports, 8, 897. https://doi.org/10.1038/s41598-018-19345-7
  • Ferruz, N., Tresadern, G., Pineda-Lucena, A., & De Fabritiis, G. (2016). Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme. Scientific Reports, 6, 30275. https://doi.org/10.1038/srep30275
  • Ferruz, N., & De Fabritiis, G. (2016). Binding kinetics in drug discovery. Molecular Informatics, 35, 392-399. https://doi.org/10.1002/minf.201501018
  • Giorgino, T., Buch, I., & De Fabritiis, G. (2012). Visualizing the induced binding of SH2-phosphopeptide. Journal of Chemical Theory and Computation, 8, 1171-1175. https://doi.org/10.1021/ct300003f
  • Buch, I., Giorgino, T., & De Fabritiis, G. (2011). Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proceedings of the National Academy of Sciences, 108, 10184-10189. https://doi.org/10.1073/pnas.1103547108
  • Buch, I., Sadiq, K., & De Fabritiis, G. (2011). Optimized potential of mean force calculations of standard binding free energy. Journal of Chemical Theory and Computation, 7, 1765-1772. https://doi.org/10.1021/ct2000638

 

Fragment Based Drug Discovery
  • Martinez-Rosell, G., Harvey, M. J., & De Fabritiis, G. (2018). Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors. Journal of Chemical Information and Modeling, 58(2), 436-440. https://doi.org/10.1021/acs.jcim.7b00625
  • Ferruz, N., Harvey, M., Mestres, J., & De Fabritiis, G. (2015). Insights from Fragment Hit Binding Assays by Molecular Simulations. Journal of Chemical Information and Modeling, 55(10), 2200-2205. https://doi.org/10.1021/acs.jcim.5b00453
  • Martí-Solano, M., Iglesias, A., De Fabritiis, G., Sanz, F., Brea, J., Loza, M., Pastor, M., & Selent, J. (2015). Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Molecular Pharmacology, 87(4), 740-746. https://doi.org/10.1124/mol.114.097022

 

Membrane Proteins
  • Stanley, N., Pardo, L., & De Fabritiis, G. (2016). The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor. Scientific Reports, 6, 22639. https://doi.org/10.1038/srep22639
  • Selent, J., Sanz, F., Pastor, M., & De Fabritiis, G. (2010). Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors. PLoS Computational Biology, 6(8), e1000884. https://doi.org/10.1371/journal.pcbi.1000884

 

Theory
  • M. J. Harvey and G. De Fabritiis. (2012). High-throughput molecular dynamics: The powerful new tool for drug discovery. Drug Discovery Today, https://doi.org/10.1016/j.drudis.2012.03.017
  • M. J. Harvey and G. De Fabritiis. (2009). An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware. J. Chem. Theory Comput., 5, 2371–2377. https://doi.org/10.1021/ct900275y
  • G. Giupponi, M. Harvey and G. De Fabritiis. (2008). The impact of accelerator processors for high-throughput molecular modeling and simulation. Drug Discovery Today, 13, 1052. https://doi.org/10.1016/j.drudis.2008.08.001

 

ACEMD/HTMD/AceCloud
  • Doerr, S., Harvey, M. J., Noé, F., & De Fabritiis, G. (2016). HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. Journal of Chemical Theory and Computation, 12(4), 1845-1852. https://doi.org/10.1021/acs.jctc.6b00049
  • Harvey, M. J., & De Fabritiis, G. (2015). AceCloud: Molecular Dynamics Simulations in the Cloud. Journal of Chemical Information and Modeling, 55(5), 909-914. https://doi.org/10.1021/acs.jcim.5b00086
  • Doerr, S., & De Fabritiis, G. (2014). On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. Journal of Chemical Theory and Computation, 10(5), 2064-2069. https://doi.org/10.1021/ct400919u
  • Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013). Identification of slow molecular order parameters for Markov model construction. The Journal of Chemical Physics, 139(1), 015102. https://doi.org/10.1063/1.4811489
  • Harvey, M., Giupponi, G., & De Fabritiis, G. (2009). ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale. Journal of Chemical Theory and Computation, 5(6), 1632-1639. https://doi.org/10.1021/ct9000685
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