R&D
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. PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. J Chem Inf Model, 2022; 62(2): 225-231 . PMID: 34978201 . DOI: 10.1021/acs.jcim.1c00691.
- Doerr S, Majewski M, Pérez A, Krämer A, Clementi C, Noe F, Giorgino T, De Fabritiis G. TorchMD: A Deep Learning Framework for Molecular Simulations. J Chem Theory Comput, 2021; 13;17(4): 2355-2363. PMID: 33729795 . DOI: 10.1021/acs.jctc.0c01343.
- Varela-Rial A, Majewski M, Cuzzolin A, Martínez-Rosell G, De Fabritiis, G. SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J Chem Inf Model, 2020, DOI: 10.1021/acs.jcim.0c00143
- Martinez-Rosell G, Lovera S, Sands ZA, De Fabritiis G. PlayMolecule CrypticScout: Predicting Protein Cryptic Sites using Mixed-Solvent Molecular Simulations. J Chem Inf Model, 2020 DOI: 10.1021/acs.jcim.9b01209.
- 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
- Miha Skalic, José Jiménez Luna, Davide Sabbadin, and Gianni De Fabritiis; Shape-Based Generative Modeling for de-novo Drug Design, Journal of Chemical Information and Modeling, (2019). DOI: 10.1021/acs.jcim.8b00706
- José Jiménez, Davide Sabbadin, Alberto Cuzzolin, Gerard Martínez-Rosell, Jacob Gora, John Manchester, José Duca, and Gianni De Fabritiis; PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks, Journal of Chemical Information and Modeling, (2018). DOI: 10.1021/acs.jcim.8b00711
- Miha Skalic, Gerard Martínez-Rosell, José Jiménez, Gianni De Fabritiis; PlayMolecule BindScope: Large scale CNN-based virtual screening on the web, Bioinformatics, (2018) , bty758, https://doi.org/10.1093/bioinformatics/bty758
- Miha Skalic, Alejandro Varela-Rial, José Jiménez, Gerard Martínez-Rosell, Gianni De Fabritiis; LigVoxel: Inpainting binding pockets using 3D-convolutional neural networks, Bioinformatics (2018) https://doi.org/10.1093/bioinformatics/bty583
- José Jiménez Luna, Miha Skalic, Gerard Martinez-Rosell, and Gianni De Fabritiis. KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. Journal of Chemical Information and Modeling. (2018) DOI: 10.1021/acs.jcim.7b00650
- J. Jiménez, S. Doerr, G. Martinez-Rosell, A.S. Rose, G. de Fabritiis. DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Structural Bioinformatics, 2017, https://doi.org/10.1093/bioinformatics/btx350
- G. Martinez-Rosell, T. Giorgino, G. de Fabritiis; PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations. J. Chem. Inf. Model (2017).https://pubs.acs.org/doi/10.1021/acs.jcim.7b00190
- N Plattner, S Doerr, G de Fabritiis, F Noé; Complete protein-protein association kinetics in atomic details revealed by molecular dynamics simulations and Markov modelling. Nature Chemistry (2017). https://www.nature.com/articles/nchem.2785
- G Martinez-Rosell, T Giorgino, MJ Harvey, G de Fabritiis; Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale. Curr Top Med Chem (2017) https://pubmed.ncbi.nlm.nih.gov/28413955/.
- E. Dainese, G. De Fabritiis, A. Sabatucci, S. Oddi, CB. Angelucci, C. Di Pancrazio, T. Giorgino, N. Stanley, M Del Carlo, B.F. Cravatt, M. Maccarrone; Membrane lipids are key modulators of the endocannabinoid-hydrolase FAAH. Biochem J. (2014) https://pubmed.ncbi.nlm.nih.gov/24215562/
- T. Giorgino and G. De Fabritiis; A high-throughput steered molecular dynamics study on the free energy profile of ion permeation through gramicidin A. J. Chem. Theory Comput., 7 , 1943–1950 (2011). https://pubs.acs.org/doi/10.1021/ct100707s
- I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson and G. De Fabritiis; High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. and Mod. 50, 397 (2010). https://pubs.acs.org/doi/10.1021/ci900455r
- N. Stanley, S. Esteban-Martín, G. De Fabritiis; Kinetic modulation of a disordered protein domain by phosphorylation. Nat Commun. 2014 Oct 28;5:5272. https://www.nature.com/articles/ncomms6272
- B. M. Satelle, B. Bose-Basu, M. Tessier, R. J. Woods, A. S. Serianni, A. Almond; Dependence of Pyranose Ring Puckering on Anomeric Configuration: Methyl Idopyranosides, J. Phys. Chem. B., 2012, 116, 6380-6386. https://pubs.acs.org/doi/10.1021/jp303183y
- S. K. Sadiq, F. Noé, G. De Fabritiis; Kinetic characterization of the critical step in HIV-1 protease maturation, Proc. Natl. Acad. Sci. USA. 2012, 109 (50), 20449-20454. https://www.pnas.org/content/109/50/20449
- D. W. Wright, S. K. Sadiq, G. De Fabritiis, P. V. Coveney; Thumbs Down for HIV: Domain Level Rearrangements Do Occur in the NNRTI-Bound HIV-1 Reverse Transcriptase, J. Am. Chem. Soc., 2012, 134 (31), 12885–12888. https://pubs.acs.org/doi/abs/10.1021/ja301565k
- K. Sadiq and G. De Fabritiis; Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing, Proteins, 78, 2873 (2010). https://pubmed.ncbi.nlm.nih.gov/20715057/
- Varela-Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. Wiley Interdisciplinary Reviews-Computational Molecular Science, 2021; e1544 DOI: 10.1002/wcms.1544.
- N Ferruz, S Doerr, M A Vanase-Frawley, Y Zou, X Chen, E S Marr, R T Nelson, B L Kormos, T T Wager, X Hou, A Villalobos, S Sciabola & G De Fabritiis, Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs, in Sci Rep. 2018; 8:897. https://www.nature.com/articles/s41598-018-19345-7
- N Ferruz, G Tresadern, A Pineda-Lucena, G De Fabritiis, Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme, in Sci Rep. 2016; 6: 30275. http://www.nature.com/articles/srep30275
- N Ferruz, G De Fabritiis, Binding Kinetics in Drug Discovery. Mol. Inf.2016 doi: 10.1002/minf.201501018
- T. Giorgino, I. Buch and G. De Fabritiis, Visualizing the induced binding of SH2-phosphopeptide, J. Chem. Theory Comput., 8, 1171–1175 (2012) http://pubs.acs.org/doi/pdfplus/10.1021/ct300003f
- I. Buch, T. Giorgino and G. De Fabritiis, Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations, PNAS, 108, 10184-10189, (2011). https://www.pnas.org/content/108/25/10184
- I. Buch, K. Sadiq and G. De Fabritiis, Optimized potential of mean force calculations of standard binding free energy, J. Chem. Theory Comput., 7, 1765–1772 (2011). https://pubs.acs.org/doi/10.1021/ct2000638
- Gerard Martinez-Rosell, Matt J. Harvey, and Gianni De Fabritiis, Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors; in Journal of Chemical Information and Modeling. DOI: 10.1021/acs.jcim.7b00625 https://pubs.acs.org/doi/10.1021/acs.jcim.7b00625
- N Ferruz, M Harvey, J Mestres, G De Fabritiis, Insights from Fragment Hit Binding Assays by Molecular Simulations, J. Chem. Inf. Model., 2015, 55 (10), pp 2200–2205 https://pubs.acs.org/doi/10.1021/acs.jcim.5b00453
- M. Martí-Solano, A. Iglesias, G. de Fabritiis, F. Sanz, J. Brea, M. Loza, M. Pastor, J. Selent. Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Mol Pharmacol. 2015 Apr;87(4):740-6. https://molpharm.aspetjournals.org/content/87/4/740.long
- N Stanley, L Pardo, G De Fabritiis, The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor, Sci Rep. 2016; 6: 22639. http://www.nature.com/articles/srep22639
- J. Selent, F. Sanz, M. Pastor and G. De Fabritiis, Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors, PLoS Comput Biol. Aug 12;6(8). pii: e1000884, (2010). https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000884
- M. J. Harvey and G. De Fabritiis, High-throughput molecular dynamics: The powerful new tool for drug discovery, Drug Discovery Today, http://dx.doi.org/10.1016/j.drudis.2012.03.017 (2012).
- M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). https://pubs.acs.org/doi/10.1021/ct900275y
- M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). https://pubs.acs.org/doi/10.1021/ct9000685
- G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation, Drug Discovery Today, 13, 1052 (2008). https://www.sciencedirect.com/science/article/abs/pii/S135964460800278X
- S. Doerr, MJ. Harvey, F. Noé and G. De Fabritiis. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput. (2016) 12 (4), pp 1845-1852. http://pubs.acs.org/doi/10.1021/acs.jctc.6b00049
- M. J. Harvey and G. De Fabritiis. AceCloud: Molecular Dynamics Simulations in the Cloud. J. Chem. Inf. Model. (2015) 55 (5), pp 909–914. https://pubs.acs.org/doi/10.1021/acs.jcim.5b00086
- S. Doerr and G. De Fabritiis. On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. J. Chem. Theory Comput. (2014) 10 (5), pp 2064–2069. https://pubs.acs.org/doi/10.1021/ct400919u
- G. Pérez-Hernández, F. Paul, T. Giorgino, G. De Fabritiis, F. Noé. Identification of slow molecular order parameters for Markov model construction., J Chem Phys (2013) Jul 7;139(1):015102. https://aip.scitation.org/doi/10.1063/1.4811489
- M. J. Harvey and G. De Fabritiis, High-throughput molecular dynamics: The powerful new tool for drug discovery, Drug Discovery Today, http://dx.doi.org/10.1016/j.drudis.2012.03.017 (2012).
- M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). https://pubs.acs.org/doi/10.1021/ct900275y
- M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). https://pubs.acs.org/doi/10.1021/ct9000685
- G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation, Drug Discovery Today, 13, 1052 (2008). https://www.sciencedirect.com/science/article/abs/pii/S135964460800278X
Applications
- Varela-Rial A, Maryanow I, Majewski M, Doerr S, Schapin N, Jiménez-Luna J, De Fabritiis G. PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. J Chem Inf Model, 2022; 62(2): 225-231 . PMID: 34978201 . DOI: 10.1021/acs.jcim.1c00691.
- Doerr S, Majewski M, Pérez A, Krämer A, Clementi C, Noe F, Giorgino T, De Fabritiis G. TorchMD: A Deep Learning Framework for Molecular Simulations. J Chem Theory Comput, 2021; 13;17(4): 2355-2363. PMID: 33729795 . DOI: 10.1021/acs.jctc.0c01343.
- Varela-Rial A, Majewski A, Cuzzolin A, Martínez-Rosell G, De Fabritiis, G. SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J Chem Inf Model, 2020, DOI: 10.1021/acs.jcim.0c00143.
- Martinez-Rosell G, Lovera S, Sands ZA, De Fabritiis G. PlayMolecule CrypticScout: Predicting Protein Cryptic Sites using Mixed-Solvent Molecular Simulations. J Chem Inf Model, 2020 DOI: 10.1021/acs.jcim.9b01209.
- 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
- Miha Skalic, José Jiménez Luna, Davide Sabbadin, and Gianni De Fabritiis; Shape-Based Generative Modeling for de-novo Drug Design, Journal of Chemical Information and Modeling, (2019). DOI: 10.1021/acs.jcim.8b00706
- José Jiménez, Davide Sabbadin, Alberto Cuzzolin, Gerard Martínez-Rosell, Jacob Gora, John Manchester, José Duca, and Gianni De Fabritiis; PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks, Journal of Chemical Information and Modeling, (2018). DOI: 10.1021/acs.jcim.8b00711
- Miha Skalic, Gerard Martínez-Rosell, José Jiménez, Gianni De Fabritiis; PlayMolecule BindScope: Large scale CNN-based virtual screening on the web, Bioinformatics, (2018) , bty758, https://doi.org/10.1093/bioinformatics/bty758
- Miha Skalic, Alejandro Varela-Rial, José Jiménez, Gerard Martínez-Rosell, Gianni De Fabritiis; LigVoxel: Inpainting binding pockets using 3D-convolutional neural networks, Bioinformatics (2018) https://doi.org/10.1093/bioinformatics/bty583
- José Jiménez Luna, Miha Skalic, Gerard Martinez-Rosell, and Gianni De Fabritiis. KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. Journal of Chemical Information and Modeling. (2018) DOI: 10.1021/acs.jcim.7b00650
- J. Jiménez, S. Doerr, G. Martinez-Rosell, A.S. Rose, G. de Fabritiis. DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Structural Bioinformatics, 2017, https://doi.org/10.1093/bioinformatics/btx350
- G. Martinez-Rosell, T. Giorgino, G. de Fabritiis; PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations. J. Chem. Inf. Model (2017).https://pubs.acs.org/doi/10.1021/acs.jcim.7b00190
- N Plattner, S Doerr, G de Fabritiis, F Noé; Complete protein-protein association kinetics in atomic details revealed by molecular dynamics simulations and Markov modelling. Nature Chemistry (2017). https://www.nature.com/articles/nchem.2785
- G Martinez-Rosell, T Giorgino, MJ Harvey, G de Fabritiis; Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale. Curr Top Med Chem (2017) https://pubmed.ncbi.nlm.nih.gov/28413955/.
- E. Dainese, G. De Fabritiis, A. Sabatucci, S. Oddi, CB. Angelucci, C. Di Pancrazio, I. Giorgino, N. Stanley, M Del Carlo, B.F. Cravatt, M. Maccarrone; Membrane lipids are key modulators of the endocannabinoid-hydrolase FAAH. Biochem J. (2014) https://pubmed.ncbi.nlm.nih.gov/24215562/
- T. Giorgino and G. De Fabritiis; A high-throughput steered molecular dynamics study on the free energy profile of ion permeation through gramicidin A. J. Chem. Theory Comput., 7 , 1943–1950 (2011). https://pubs.acs.org/doi/10.1021/ct100707s
- I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson and G. De Fabritiis; High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. and Mod. 50, 397 (2010). https://pubs.acs.org/doi/10.1021/ci900455r
Conformational Studies
- N. Stanley, S. Esteban-Martín, G. De Fabritiis; Kinetic modulation of a disordered protein domain by phosphorylation. Nat Commun. 2014 Oct 28;5:5272. https://www.nature.com/articles/ncomms6272
- B. M. Satelle, B. Bose-Basu, M. Tessier, R. J. Woods, A. S. Serianni, A. Almond; Dependence of Pyranose Ring Puckering on Anomeric Configuration: Methyl Idopyranosides, J. Phys. Chem. B., 2012, 116, 6380-6386. https://pubs.acs.org/doi/10.1021/jp303183y
- S. K. Sadiq, F. Noé, G. De Fabritiis; Kinetic characterization of the critical step in HIV-1 protease maturation, Proc. Natl. Acad. Sci. USA. 2012, 109 (50), 20449-20454. https://www.pnas.org/content/109/50/20449
- D. W. Wright, S. K. Sadiq, G. De Fabritiis, P. V. Coveney; Thumbs Down for HIV: Domain Level Rearrangements Do Occur in the NNRTI-Bound HIV-1 Reverse Transcriptase, J. Am. Chem. Soc., 2012, 134 (31), 12885–12888. https://pubs.acs.org/doi/abs/10.1021/ja301565k
- K. Sadiq and G. De Fabritiis; Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing, Proteins, 78, 2873 (2010). https://pubmed.ncbi.nlm.nih.gov/20715057/
Protein-Ligand Binding
- Varela-Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. Wiley Interdisciplinary Reviews-Computational Molecular Science, 2021; e1544 DOI: 10.1002/wcms.1544.
- N Ferruz, S Doerr, M A Vanase-Frawley, Y Zou, X Chen, E S Marr, R T Nelson, B L Kormos, T T Wager, X Hou, A Villalobos, S Sciabola & G De Fabritiis, Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs, in Sci Rep. 2018; 8:897. https://www.nature.com/articles/s41598-018-19345-7
- N Ferruz, G Tresadern, A Pineda-Lucena, G De Fabritiis, Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme, in Sci Rep. 2016; 6: 30275. http://www.nature.com/articles/srep30275
- N Ferruz, G De Fabritiis, Binding Kinetics in Drug Discovery. Mol. Inf.2016 doi: 10.1002/minf.201501018
- T. Giorgino, I. Buch and G. De Fabritiis, Visualizing the induced binding of SH2-phosphopeptide, J. Chem. Theory Comput., 8, 1171–1175 (2012) http://pubs.acs.org/doi/pdfplus/10.1021/ct300003f
- I. Buch, T. Giorgino and G. De Fabritiis, Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations, PNAS, 108, 10184-10189, (2011). https://www.pnas.org/content/108/25/10184
- I. Buch, K. Sadiq and G. De Fabritiis, Optimized potential of mean force calculations of standard binding free energy, J. Chem. Theory Comput., 7, 1765–1772 (2011). https://pubs.acs.org/doi/10.1021/ct2000638
Fragment-Based Drug Discovery
- Gerard Martinez-Rosell, Matt J. Harvey, and Gianni De Fabritiis, Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors; in Journal of Chemical Information and Modeling. DOI: 10.1021/acs.jcim.7b00625 https://pubs.acs.org/doi/10.1021/acs.jcim.7b00625
- N Ferruz, M Harvey, J Mestres, G De Fabritiis, Insights from Fragment Hit Binding Assays by Molecular Simulations, J. Chem. Inf. Model., 2015, 55 (10), pp 2200–2205 https://pubs.acs.org/doi/10.1021/acs.jcim.5b00453
- M. Martí-Solano, A. Iglesias, G. de Fabritiis, F. Sanz, J. Brea, M. Loza, M. Pastor, J. Selent. Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Mol Pharmacol. 2015 Apr;87(4):740-6. https://molpharm.aspetjournals.org/content/87/4/740.long
Membrane Proteins
- N Stanley, L Pardo, G De Fabritiis, The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor, Sci Rep. 2016; 6: 22639. http://www.nature.com/articles/srep22639
- J. Selent, F. Sanz, M. Pastor and G. De Fabritiis, Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors, PLoS Comput Biol. Aug 12;6(8). pii: e1000884, (2010). https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000884
Theory
- M. J. Harvey and G. De Fabritiis, High-throughput molecular dynamics: The powerful new tool for drug discovery, Drug Discovery Today, http://dx.doi.org/10.1016/j.drudis.2012.03.017 (2012).
- M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). https://pubs.acs.org/doi/10.1021/ct900275y
- M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). https://pubs.acs.org/doi/10.1021/ct9000685
- G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation, Drug Discovery Today, 13, 1052 (2008). https://www.sciencedirect.com/science/article/abs/pii/S135964460800278X
ACEMD
- M. J. Harvey and G. De Fabritiis, High-throughput molecular dynamics: The powerful new tool for drug discovery, Drug Discovery Today, http://dx.doi.org/10.1016/j.drudis.2012.03.017 (2012).
- M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). https://pubs.acs.org/doi/10.1021/ct900275y
- M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). https://pubs.acs.org/doi/10.1021/ct9000685
- G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation, Drug Discovery Today, 13, 1052 (2008). https://www.sciencedirect.com/science/article/abs/pii/S135964460800278X
HTMD
- S. Doerr, MJ. Harvey, F. Noé and G. De Fabritiis. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput. (2016) 12 (4), pp 1845-1852. http://pubs.acs.org/doi/10.1021/acs.jctc.6b00049
- S. Doerr and G. De Fabritiis. On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. J. Chem. Theory Comput. (2014) 10 (5), pp 2064–2069. https://pubs.acs.org/doi/10.1021/ct400919u
- G. Pérez-Hernández, F. Paul, T. Giorgino, G. De Fabritiis, F. Noé. Identification of slow molecular order parameters for Markov model construction., J Chem Phys (2013) Jul 7;139(1):015102. https://aip.scitation.org/doi/10.1063/1.4811489
AceCloud
M. J. Harvey and G. De Fabritiis. AceCloud: Molecular Dynamics Simulations in the Cloud. J. Chem. Inf. Model. (2015) 55 (5), pp 909–914. https://pubs.acs.org/doi/10.1021/acs.jcim.5b00086
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