| scholarly article | Q13442814 |
| P50 | author | Ching-Feng Weng | Q43275475 |
| P2093 | author name string | Max K Leong | |
| Yi-Lung Ding | |||
| Ren-Guei Syu | |||
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| Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming | Q34447822 | ||
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| Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise | Q35981309 | ||
| Describing the validity of carcinogen screening tests | Q36042747 | ||
| Agonist binding to the NMDA receptor drives movement of its cytoplasmic domain without ion flow. | Q36332086 | ||
| Consensus scoring for protein-ligand interactions | Q36458866 | ||
| What is a support vector machine? | Q36679246 | ||
| Protons Potentiate GluN1/GluN3A Currents by Attenuating Their Desensitisation. | Q36713681 | ||
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| Rescoring ligand docking poses. | Q37742544 | ||
| Structural ensemble in computational drug screening | Q37750881 | ||
| GluN3 subunit-containing NMDA receptors: not just one-trick ponies | Q37975344 | ||
| Latest developments in molecular docking: 2010–2011 in review | Q38092807 | ||
| Targeting of NMDA receptors in new treatments for schizophrenia | Q38223276 | ||
| NMDARs in neurological diseases: a potential therapeutic target | Q38232734 | ||
| In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models | Q39525606 | ||
| Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations | Q39795344 | ||
| Synthesis, structural activity-relationships, and biological evaluation of novel amide-based allosteric binding site antagonists in NR1A/NR2B N-methyl-D-aspartate receptors | Q39818021 | ||
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| Consensus scoring for ligand/protein interactions | Q42670601 | ||
| QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants | Q43090710 | ||
| Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments | Q44012277 | ||
| CoMFA and homology-based models of the glycine binding site of N-methyl-d-aspartate receptor | Q44406084 | ||
| Comparative evaluation of 11 scoring functions for molecular docking | Q44457437 | ||
| The ensemble performance index: an improved measure for assessing ensemble pose prediction performance. | Q44636889 | ||
| Model structures of the N-methyl-D-aspartate receptor subunit NR1 explain the molecular recognition of agonist and antagonist ligands | Q44762506 | ||
| SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes | Q45805013 | ||
| In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure-Property Relationship Models. | Q45955189 | ||
| Predicting mutagenicity of aromatic amines by various machine learning approaches. | Q45962910 | ||
| Protein docking using surface matching and supervised machine learning. | Q45965397 | ||
| Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. | Q45966697 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | ligand binding | Q61659151 |
| P304 | page(s) | 40053 | |
| P577 | publication date | 2017-01-06 | |
| P1433 | published in | Scientific Reports | Q2261792 |
| P1476 | title | Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme | |
| P478 | volume | 7 |
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