Works matching DE "PROTEIN-ligand interactions"
Results: 1034
Detecting Protein‐Ligand Interactions with Nitroxide Based Paramagnetic Cosolutes.
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- Chemistry - A European Journal, 2024, v. 30, n. 18, p. 1, doi. 10.1002/chem.202303570
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Alkyne Activation in the Diversity Oriented Synthesis of sp<sup>2</sup>‐Rich Scaffolds: A Biased Library Approach for Targeting Polynucleotides (DNA/RNA).
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- Chemistry - A European Journal, 2022, v. 28, n. 71, p. 1, doi. 10.1002/chem.202201925
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Multi-targeted benzylpiperidine–isatin hybrids: Design, synthesis, biological and in silico evaluation as monoamine oxidases and acetylcholinesterase inhibitors for neurodegenerative disease therapies.
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- Journal of Computer-Aided Molecular Design, 2025, v. 39, n. 1, p. 1, doi. 10.1007/s10822-025-00588-2
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Enhancement by pyrazolones of colistin efficacy against mcr-1-expressing E. coli: an in silico and in vitro investigation.
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- Journal of Computer-Aided Molecular Design, 2023, v. 37, n. 10, p. 479, doi. 10.1007/s10822-023-00519-z
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Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant.
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- Journal of Computer-Aided Molecular Design, 2022, v. 36, n. 12, p. 879, doi. 10.1007/s10822-022-00487-w
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Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.
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- Journal of Computer-Aided Molecular Design, 2022, v. 36, n. 10, p. 767, doi. 10.1007/s10822-022-00479-w
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Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: I. Standard procedure.
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- Journal of Computer-Aided Molecular Design, 2022, v. 36, n. 10, p. 735, doi. 10.1007/s10822-022-00475-0
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 11, p. 1095, doi. 10.1007/s10822-021-00423-4
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Encoding mu-opioid receptor biased agonism with interaction fingerprints.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 11, p. 1081, doi. 10.1007/s10822-021-00422-5
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Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 5, p. 643, doi. 10.1007/s10822-021-00382-w
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Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 4, p. 453, doi. 10.1007/s10822-020-00347-5
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Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 4, p. 557, doi. 10.1007/s10822-020-00346-6
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A binding mode hypothesis for prothioconazole binding to CYP51 derived from first principles quantum chemistry.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 4, p. 493, doi. 10.1007/s10822-020-00331-z
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Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein–ligand interactions and quantitative structure–activity relationship studies.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 3, p. 371, doi. 10.1007/s10822-021-00377-7
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Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 2, p. 131, doi. 10.1007/s10822-020-00362-6
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QSAR and molecular docking for the search of AOX inhibitors: a rational drug discovery approach.
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- Journal of Computer-Aided Molecular Design, 2021, v. 35, n. 2, p. 245, doi. 10.1007/s10822-020-00360-8
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Novel phosphatidylinositol 4-kinases III beta (PI4KIIIβ) inhibitors discovered by virtual screening using free energy models.
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- Journal of Computer-Aided Molecular Design, 2020, v. 34, n. 10, p. 1091, doi. 10.1007/s10822-020-00327-9
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Improving the binding affinity estimations of protein–ligand complexes using machine-learning facilitated force field method.
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- Journal of Computer-Aided Molecular Design, 2020, v. 34, n. 8, p. 817, doi. 10.1007/s10822-020-00305-1
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D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.
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- Journal of Computer-Aided Molecular Design, 2020, v. 34, n. 2, p. 163, doi. 10.1007/s10822-019-00249-1
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The role of human in the loop: lessons from D3R challenge 4.
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- Journal of Computer-Aided Molecular Design, 2020, v. 34, n. 2, p. 121, doi. 10.1007/s10822-020-00291-4
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Source of oseltamivir resistance due to single E119D and double E119D/H274Y mutations in pdm09H1N1 influenza neuraminidase.
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- Journal of Computer-Aided Molecular Design, 2020, v. 34, n. 1, p. 27, doi. 10.1007/s10822-019-00251-7
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Blinded prediction of protein–ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4.
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- Journal of Computer-Aided Molecular Design, 2019, v. 33, n. 12, p. 1021, doi. 10.1007/s10822-019-00223-x
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Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.
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- Journal of Computer-Aided Molecular Design, 2019, v. 33, n. 12, p. 1083, doi. 10.1007/s10822-019-00222-y
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Water molecules in protein-ligand interfaces. Evaluation of software tools and SAR comparison.
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- Journal of Computer-Aided Molecular Design, 2019, v. 33, n. 3, p. 307, doi. 10.1007/s10822-019-00187-y
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Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight.
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- Journal of Computer-Aided Molecular Design, 2019, v. 33, n. 3, p. 367, doi. 10.1007/s10822-019-00185-0
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Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.
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- Journal of Computer-Aided Molecular Design, 2019, v. 33, n. 1, p. 105, doi. 10.1007/s10822-018-0162-6
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Theoretical models to predict the inhibitory effect of ligands of sphingosine kinase 1 using QTAIM calculations and hydrogen bond dynamic propensity analysis.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 7, p. 781, doi. 10.1007/s10822-018-0129-7
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Influence of gauche effect on uncharged oxime reactivators for the reactivation of tabun-inhibited AChE: quantum chemical and steered molecular dynamics studies.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 7, p. 793, doi. 10.1007/s10822-018-0130-1
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Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 7, p. 759, doi. 10.1007/s10822-018-0128-8
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Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.
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- Journal of Computer-Aided Molecular Design, 2018, v. 32, n. 7, p. 731, doi. 10.1007/s10822-018-0126-x
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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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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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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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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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Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 10, p. 943, doi. 10.1007/s10822-017-0068-8
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Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015.
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 8, p. 689, doi. 10.1007/s10822-017-0038-1
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GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking.
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 7, p. 653, doi. 10.1007/s10822-017-0030-9
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Assessing protein-ligand binding modes with computational tools: the case of PDE4B.
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 6, p. 563, doi. 10.1007/s10822-017-0024-7
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Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations.
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 2, p. 201, doi. 10.1007/s10822-016-0005-2
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Overview of the SAMPL5 host-guest challenge: Are we doing better?
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- Journal of Computer-Aided Molecular Design, 2017, v. 31, n. 1, p. 1, doi. 10.1007/s10822-016-9974-4
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Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 1019, doi. 10.1007/s10822-016-9928-x
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Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and ARROW polarizable force fields.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 977, doi. 10.1007/s10822-016-9958-4
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SAMPL5: 3D-RISM partition coefficient calculations with partial molar volume corrections and solute conformational sampling.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 1115, doi. 10.1007/s10822-016-9947-7
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Predicting water-to-cyclohexane partitioning of the SAMPL5 molecules using dielectric balancing of force fields.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 1059, doi. 10.1007/s10822-016-9950-z
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Calculation of distribution coefficients in the SAMPL5 challenge from atomic solvation parameters and surface areas.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 1079, doi. 10.1007/s10822-016-9951-y
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Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.
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- Journal of Computer-Aided Molecular Design, 2016, v. 30, n. 11, p. 927, doi. 10.1007/s10822-016-9954-8
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