Works in Journal of Computer-Aided Molecular Design, 2018, Vol 32, Issue 1
Results: 24
A cross docking pipeline for improving pose prediction and virtual screening performance.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 163, doi. 10.1007/s10822-017-0048-z
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Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 175, doi. 10.1007/s10822-017-0049-y
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CDOCKER and $$\lambda$$ -dynamics for prospective prediction in D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 89, doi. 10.1007/s10822-017-0050-5
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Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 251, doi. 10.1007/s10822-017-0051-4
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Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 113, doi. 10.1007/s10822-017-0053-2
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Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 273, doi. 10.1007/s10822-017-0054-1
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Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 239, doi. 10.1007/s10822-017-0055-0
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Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 211, doi. 10.1007/s10822-017-0056-z
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Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 187, doi. 10.1007/s10822-017-0058-x
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Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 151, doi. 10.1007/s10822-017-0062-1
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Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 231, doi. 10.1007/s10822-017-0063-0
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Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 265, doi. 10.1007/s10822-017-0064-z
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Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 287, doi. 10.1007/s10822-017-0065-y
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Protein-ligand docking using FFT based sampling: D3R case study.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 225, doi. 10.1007/s10822-017-0069-7
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Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 143, doi. 10.1007/s10822-017-0071-0
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Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 129, doi. 10.1007/s10822-017-0072-z
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Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 59, doi. 10.1007/s10822-017-0074-x
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Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 21, doi. 10.1007/s10822-017-0075-9
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Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 45, doi. 10.1007/s10822-017-0081-y
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Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 103, doi. 10.1007/s10822-017-0082-x
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Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 199, doi. 10.1007/s10822-017-0083-9
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Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 299, doi. 10.1007/s10822-017-0085-7
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D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 1, doi. 10.1007/s10822-017-0088-4
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Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 1, p. 75, doi. 10.1007/s10822-017-0046-1
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