Cuzzolin A - Search Results

1

Implementing the "Best Template Searching" tool into Adenosiland platform.

Floris M, Sabbadin D, Ciancetta A, Medda R, Cuzzolin A, Moro S. Floris M, et al. Among authors: cuzzolin a. In Silico Pharmacol. 2013 Dec 20;1:25. doi: 10.1186/2193-9616-1-25. eCollection 2013. In Silico Pharmacol. 2013. PMID: 25505667 Free PMC article.

2

Alternative quality assessment strategy to compare performances of GPCR-ligand docking protocols: the human adenosine A(2A) receptor as a case study.

Ciancetta A, Cuzzolin A, Moro S. Ciancetta A, et al. Among authors: cuzzolin a. J Chem Inf Model. 2014 Aug 25;54(8):2243-54. doi: 10.1021/ci5002857. Epub 2014 Jul 31. J Chem Inf Model. 2014. PMID: 25046649

3

Understanding allosteric interactions in G protein-coupled receptors using Supervised Molecular Dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000.

Deganutti G, Cuzzolin A, Ciancetta A, Moro S. Deganutti G, et al. Among authors: cuzzolin a. Bioorg Med Chem. 2015 Jul 15;23(14):4065-71. doi: 10.1016/j.bmc.2015.03.039. Epub 2015 Mar 20. Bioorg Med Chem. 2015. PMID: 25868747

4

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Cuzzolin A, Sturlese M, Deganutti G, Salmaso V, Sabbadin D, Ciancetta A, Moro S. Cuzzolin A, et al. J Chem Inf Model. 2016 Apr 25;56(4):687-705. doi: 10.1021/acs.jcim.5b00702. Epub 2016 Apr 14. J Chem Inf Model. 2016. PMID: 27019343

5

New Trends in Inspecting GPCR-ligand Recognition Process: the Contribution of the Molecular Modeling Section (MMS) at the University of Padova.

Ciancetta A, Cuzzolin A, Deganutti G, Sturlese M, Salmaso V, Cristiani A, Sabbadin D, Moro S. Ciancetta A, et al. Among authors: cuzzolin a. Mol Inform. 2016 Sep;35(8-9):440-8. doi: 10.1002/minf.201501011. Epub 2016 Jul 19. Mol Inform. 2016. PMID: 27546048 Review.

6

DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations.

Cuzzolin A, Sturlese M, Malvacio I, Ciancetta A, Moro S. Cuzzolin A, et al. Molecules. 2015 May 29;20(6):9977-93. doi: 10.3390/molecules20069977. Molecules. 2015. PMID: 26035098 Free PMC article.

7

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015.

Salmaso V, Sturlese M, Cuzzolin A, Moro S. Salmaso V, et al. Among authors: cuzzolin a. J Comput Aided Mol Des. 2016 Sep;30(9):773-789. doi: 10.1007/s10822-016-9966-4. Epub 2016 Sep 16. J Comput Aided Mol Des. 2016. PMID: 27638810

8

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2.

Salmaso V, Sturlese M, Cuzzolin A, Moro S. Salmaso V, et al. Among authors: cuzzolin a. J Comput Aided Mol Des. 2018 Jan;32(1):251-264. doi: 10.1007/s10822-017-0051-4. Epub 2017 Aug 24. J Comput Aided Mol Des. 2018. PMID: 28840418

9

Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach.

Salmaso V, Sturlese M, Cuzzolin A, Moro S. Salmaso V, et al. Among authors: cuzzolin a. Structure. 2017 Apr 4;25(4):655-662.e2. doi: 10.1016/j.str.2017.02.009. Epub 2017 Mar 16. Structure. 2017. PMID: 28319010 Free article.

10

AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association.

Cuzzolin A, Deganutti G, Salmaso V, Sturlese M, Moro S. Cuzzolin A, et al. ChemMedChem. 2018 Mar 20;13(6):522-531. doi: 10.1002/cmdc.201700564. Epub 2018 Jan 25. ChemMedChem. 2018. PMID: 29193885

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