Sabbadin D - Search Results

1

Computationally driven discovery of SARS-CoV-2 M^pro inhibitors: from design to experimental validation.

El Khoury L, Jing Z, Cuzzolin A, Deplano A, Loco D, Sattarov B, Hédin F, Wendeborn S, Ho C, El Ahdab D, Jaffrelot Inizan T, Sturlese M, Sosic A, Volpiana M, Lugato A, Barone M, Gatto B, Macchia ML, Bellanda M, Battistutta R, Salata C, Kondratov I, Iminov R, Khairulin A, Mykhalonok Y, Pochepko A, Chashka-Ratushnyi V, Kos I, Moro S, Montes M, Ren P, Ponder JW, Lagardère L, Piquemal JP, Sabbadin D. El Khoury L, et al. Among authors: sabbadin d. Chem Sci. 2022 Feb 10;13(13):3674-3687. doi: 10.1039/d1sc05892d. eCollection 2022 Mar 30. Chem Sci. 2022. PMID: 35432906 Free PMC article.

2

Bridging molecular docking to membrane molecular dynamics to investigate GPCR-ligand recognition: the human A₂A adenosine receptor as a key study.

Sabbadin D, Ciancetta A, Moro S. Sabbadin D, et al. J Chem Inf Model. 2014 Jan 27;54(1):169-83. doi: 10.1021/ci400532b. Epub 2014 Jan 8. J Chem Inf Model. 2014. PMID: 24359090

3

Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale.

Sabbadin D, Moro S. Sabbadin D, et al. J Chem Inf Model. 2014 Feb 24;54(2):372-6. doi: 10.1021/ci400766b. Epub 2014 Feb 3. J Chem Inf Model. 2014. PMID: 24456045

4

Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study.

Sabbadin D, Ciancetta A, Moro S. Sabbadin D, et al. J Chem Inf Model. 2014 Oct 27;54(10):2846-55. doi: 10.1021/ci500397y. Epub 2014 Oct 6. J Chem Inf Model. 2014. PMID: 25245783

5

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. Among authors: sabbadin d. 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

6

Supervised Molecular Dynamics (SuMD) Approaches in Drug Design.

Sabbadin D, Salmaso V, Sturlese M, Moro S. Sabbadin D, et al. Methods Mol Biol. 2018;1824:287-298. doi: 10.1007/978-1-4939-8630-9_17. Methods Mol Biol. 2018. PMID: 30039414

7

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: sabbadin d. Mol Inform. 2016 Sep;35(8-9):440-8. doi: 10.1002/minf.201501011. Epub 2016 Jul 19. Mol Inform. 2016. PMID: 27546048 Review.

8

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: sabbadin d. 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.

9

Advances in Computational Techniques to Study GPCR-Ligand Recognition.

Ciancetta A, Sabbadin D, Federico S, Spalluto G, Moro S. Ciancetta A, et al. Among authors: sabbadin d. Trends Pharmacol Sci. 2015 Dec;36(12):878-890. doi: 10.1016/j.tips.2015.08.006. Epub 2015 Nov 1. Trends Pharmacol Sci. 2015. PMID: 26538318 Free article. Review.

10

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Skalic M, Sabbadin D, Sattarov B, Sciabola S, De Fabritiis G. Skalic M, et al. Among authors: sabbadin d. Mol Pharm. 2019 Oct 7;16(10):4282-4291. doi: 10.1021/acs.molpharmaceut.9b00634. Epub 2019 Sep 10. Mol Pharm. 2019. PMID: 31437001

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