Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method

Journal of Computer-Aided Molecular Design

Volumn 28, Issue 4, 2014, Pages 463-474

  Muddana, Hari S ^a   Yin, Jian ^a   Sapra, Neil V ^a   Fenley, Andrew T ^a   Gilson, Michael K ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

Binding affinity; Force field; Host guest; SAMPL4; Semiempirical quantum; Supramolecular

Indexed keywords

BINDING ENERGY; ENTROPY; FORECASTING; HAMILTONIANS; QUANTUM THEORY; SOLVATION;

BINDING AFFINITIES; BLIND PREDICTIONS; ENERGY MODEL; FORCEFIELDS; HOST:GUEST; SAMPL4; SEMI-EMPIRICAL; SEMIEMPIRICAL QUANTUM; SOLVATION FREE ENERGIES; SUPRAMOLECULAR;

FREE ENERGY;

CARBENE; CUCURBIT[7]URIL; PROTEIN; UNCLASSIFIED DRUG; BRIDGED COMPOUND; CUCURBIT(7)URIL; IMIDAZOLE DERIVATIVE;

ARTICLE; BINDING AFFINITY; COMPUTER MODEL; COMPUTER PREDICTION; DATA MINING; DRUG DESIGN; DRUG PROTEIN BINDING; ENERGY; FORCE; LIGAND BINDING; MEASUREMENT ACCURACY; PRIORITY JOURNAL; QUANTUM MECHANICS; SOLVATION; SUPRAMOLECULAR CHEMISTRY; SURFACE PROPERTY; BINDING SITE; CHEMICAL MODEL; CHEMISTRY; CONFORMATION; MOLECULAR DOCKING; QUANTUM THEORY; THERMODYNAMICS;

BINDING SITES; BRIDGED COMPOUNDS; IMIDAZOLES; MODELS, CHEMICAL; MOLECULAR CONFORMATION; MOLECULAR DOCKING SIMULATION; QUANTUM THEORY; THERMODYNAMICS;

EID: 84902816202     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-014-9726-2     Document Type: Article

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