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