Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets | Zendy

Edithe Selwa | Zendy; Virginie Martiny | Zendy; Bogdan I. Iorga | Zendy

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Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

Author(s) -

Edithe Selwa,

Virginie Martiny,

Bogdan I. Iorga

Publication year - 2016

Publication title -

journal of computer-aided molecular design

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.749

H-Index - 101

eISSN - 1573-4951

pISSN - 0920-654X

DOI - 10.1007/s10822-016-9983-3

Subject(s) - pubchem , docking (animal) , computational biology , protein data bank (rcsb pdb) , drug discovery , protein data bank , computer science , kinase , protein kinase a , data mining , biology , bioinformatics , biochemistry , protein structure , medicine , nursing

The D3R Grand Challenge 2015 was focused on two protein targets: Heat Shock Protein 90 (HSP90) and Mitogen-Activated Protein Kinase Kinase Kinase Kinase 4 (MAP4K4). We used a protocol involving a preliminary analysis of the available data in PDB and PubChem BioAssay, and then a docking/scoring step using more computationally demanding parameters that were required to provide more reliable predictions. We could evidence that different docking software and scoring functions can behave differently on individual ligand datasets, and that the flexibility of specific binding site residues is a crucial element to provide good predictions.

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