AutoGrow: a novel algorithm for protein inhibitor design
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A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System TargetsSulfamethoxazole induces a switch mechanism in T cell receptors containing TCRVβ20-1, altering pHLA recognitionSearch for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulationsAutoClickChem: click chemistry in silicoACFIS: a web server for fragment-based drug discoveryExploring the chemical space of influenza neuraminidase inhibitorsPotential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design.Progress in structure-based drug design against influenza A virus.Virtual Screening and Molecular Dynamics Simulations from a Bank of Molecules of the Amazon Region Against Functional NS3-4A Protease-Helicase Enzyme of Hepatitis C Virus.Targeting YAP/TAZ-TEAD protein-protein interactions using fragment-based and computational modeling approaches.Towards the development of novel Trypanosoma brucei RNA editing ligase 1 inhibitorsIn silico discovery of potential VEGFR-2 inhibitors from natural derivatives for anti-angiogenesis therapy.Identification of dengue viral RNA-dependent RNA polymerase inhibitor using computational fragment-based approaches and molecular dynamics study.Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag.Pocket-based drug design: exploring pocket spaceAutoGrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design.Fragment informatics and computational fragment-based drug design: an overview and update.De novo design: balancing novelty and confined chemical space.Computational identification of novel entry inhibitor scaffolds mimicking primary receptor CD4 of HIV-1 gp120.Versatility of acyl-acyl carrier protein synthetases.Scoria: a Python module for manipulating 3D molecular data.Maximizing computational tools for successful drug discovery.Prediction of Positions of Active Compounds Makes It Possible To Increase Activity in Fragment-Based Drug Development.Rational Design of Highly Potent and Slow-Binding Cytochrome bc1 Inhibitor as Fungicide by Computational Substitution Optimization.
P2860
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P2860
AutoGrow: a novel algorithm for protein inhibitor design
description
2009 nî lūn-bûn
@nan
2009 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2009年の論文
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2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
AutoGrow: a novel algorithm for protein inhibitor design
@ast
AutoGrow: a novel algorithm for protein inhibitor design
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type
label
AutoGrow: a novel algorithm for protein inhibitor design
@ast
AutoGrow: a novel algorithm for protein inhibitor design
@en
prefLabel
AutoGrow: a novel algorithm for protein inhibitor design
@ast
AutoGrow: a novel algorithm for protein inhibitor design
@en
P2860
P1476
AutoGrow: a novel algorithm for protein inhibitor design
@en
P2093
Rommie E Amaro
P2860
P304
P356
10.1111/J.1747-0285.2008.00761.X
P577
2009-02-01T00:00:00Z