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chevron_left Protein-ligand docking Binding affinity prediction Stability change prediction chevron_right
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TopologyNet specifications

Information


Unique identifier OMICS_19937
Name TopologyNet
Interface Web user interface
Restrictions to use None
Input data For Soluble protein ddG: Input Protein, Chain ID, Residue, Wild Name, Mutant, Experimental pH. For Protein-ligand binding affinity: The 3D structures of protein and ligand in bound position.
Input format PDB, mol2
Computer skills Basic
Stability Beta
Maintained Yes

Maintainer


  • person_outline Guowei Wei <>

Publication for TopologyNet

TopologyNet in publication

PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387

[…] [,]., structure-based deep learning methods differ in whether they use experimentally derived or predicted ligand–target complexes and how they represent the 3d structure. the atomic cnn [] and topologynet [] models take 3d structures from pdbbind [] as input, ensuring the ligand–target complexes are reliable. atomnet [] samples multiple ligand poses within the target binding site, […]


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TopologyNet institution(s)
Department of Mathematics, Michigan State University, East Lansing, MI, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA
TopologyNet funding source(s)
Supported by National Science Foundation IIS-1302285, National Science Foundation DMS-1721024 and Michigan State University.

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