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Italian Workshop on Artificial Life and Evolutionary Computation

WIVACE 2017: Artificial Life and Evolutionary Computation pp 49–58Cite as

Fragment Based Molecular Dynamics for Drug Design

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 830))

Abstract

Molecular docking is a computationally efficient method used to predict the conformations adopted by the ligand within a target-binding site. A positive aspect of conventional docking is the possibility of easily distributing the calculation on dedicated grid or cluster. The receptor is usually kept rigid, therefore the changes in the binding pocket geometry induced by the ligand is overlooked. Here we present a new docking approach (DynDock) that exploits molecular dynamics to preserve the flexibility of the receptor. To maintain high computational efficiency, DynDock has been developed to be distributed on a grid. The main advantages of this method are the full flexible molecular docking achieved during the simulation and the reduced number of compounds collected.

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Author information

Authors and Affiliations

  1. Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Lucia Sessa, Luigi Di Biasi & Stefano Piotto

  2. Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Simona Concilio

Authors
  1. Lucia Sessa
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  2. Luigi Di Biasi
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  3. Simona Concilio
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  4. Stefano Piotto
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Corresponding author

Correspondence to Lucia Sessa .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Irene Poli

  3. University of Bologna, Cesena, Italy

    Andrea Roli

  4. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

  5. Ca’ Foscari University of Venice, Venice, Italy

    Debora Slanzi

  6. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Sessa, L., Di Biasi, L., Concilio, S., Piotto, S. (2018). Fragment Based Molecular Dynamics for Drug Design. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-78658-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78657-5

  • Online ISBN: 978-3-319-78658-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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