scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/jcisd/BenderG05 |
P356 | DOI | 10.1021/CI0500177 |
P698 | PubMed publication ID | 16180913 |
P5875 | ResearchGate publication ID | 7583053 |
P50 | author | Robert C Glen | Q28946543 |
Andreas Bender | Q28925563 | ||
P2093 | author name string | Andreas Bender | |
P2860 | cites work | Approaches to Measure Chemical Similarity– a Review | Q28531485 |
P433 | issue | 5 | |
P921 | main subject | information content | Q735075 |
virtual screening | Q4112105 | ||
P304 | page(s) | 1369-1375 | |
P577 | publication date | 2005-09-01 | |
P1433 | published in | Journal of Chemical Information and Modeling | Q3007982 |
P1476 | title | A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication | |
P478 | volume | 45 |
Q27134895 | A Survey of Quantitative Descriptions of Molecular Structure |
Q39300262 | Anti-HIV-1 Activity Prediction of Novel Gp41 Inhibitors Using Structure-Based Virtual Screening and Molecular Dynamics Simulation. |
Q27998664 | Application of 3D Zernike descriptors to shape-based ligand similarity searching |
Q34019272 | Application of NMR and molecular docking in structure-based drug discovery |
Q51304233 | AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking. |
Q92134160 | Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning |
Q50622600 | Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor. |
Q60046647 | Bioinformatics: A rational combine approach used for the identification and in-vitro activity evaluation of potent β-Glucuronidase inhibitors |
Q82908096 | Can we really do computer-aided drug design? |
Q56916101 | Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR |
Q30386769 | Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations |
Q60238441 | Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors |
Q37790487 | Computational analysis of structure-activity relationships |
Q34619242 | Computer-aided retrometabolic drug design: soft drugs |
Q92636228 | Conformational Studies of Glucose Transporter 1 (GLUT1) as an Anticancer Drug Target |
Q31163923 | Data mining using template-based molecular docking on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO) derivatives as HIV-1RT inhibitors |
Q38409890 | Dereplication: racing to speed up the natural products discovery process. |
Q36803981 | Effects of histidine protonation and rotameric states on virtual screening of M. tuberculosis RmlC. |
Q51070891 | Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors. |
Q50910256 | Ensembling and filtering: an effective and rapid in silico multitarget drug-design strategy to identify RIPK1 and RIPK3 inhibitors. |
Q36548489 | Exploration of multiple Sortase A protein conformations in virtual screening |
Q42874590 | Exploring different virtual screening strategies for acetylcholinesterase inhibitors |
Q45965607 | Feature-map vectors: a new class of informative descriptors for computational drug discovery. |
Q47114939 | High-throughput detection of antioxidants in mulberry fruit using correlations between high-resolution mass and activity profiles of chromatographic fractions |
Q57937286 | High-throughput structural characterisation of therapeutic protein targets |
Q34347842 | How similar are those molecules after all? Use two descriptors and you will have three different answers |
Q53092961 | Identification of InhA inhibitors: A combination of virtual screening, molecular dynamics simulations and quantum chemical studies. |
Q51632843 | Identification of new dual spleen tyrosine kinase (Syk) and phosphoionositide-3-kinase δ (PI3Kδ) inhibitors using ligand and structure-based integrated ideal pharmacophore models. |
Q89174818 | Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials |
Q33309163 | Integrating high-content screening and ligand-target prediction to identify mechanism of action |
Q54172917 | Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3). |
Q45383758 | Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features |
Q50955211 | Modelling the molecular mechanism of protein-protein interactions and their inhibition: CypD-p53 case study. |
Q90226731 | Molecular Modeling Studies on the Binding Mode of the PD-1/PD-L1 Complex Inhibitors |
Q47401024 | Molecular dynamics and integrated pharmacophore-based identification of dual [Formula: see text] inhibitors. |
Q47788380 | Optimization of compound ranking for structure-based virtual ligand screening using an established FRED-Surflex consensus approach |
Q39647348 | Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. |
Q42414848 | Predicting phospholipidosis using machine learning |
Q33772055 | Profiling diverse compounds by flux- and electrophysiology-based primary screens for inhibition of human Ether-à-go-go related gene potassium channels |
Q54152447 | Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets |
Q89414823 | QEX: target-specific druglikeness filter enhances ligand-based virtual screening |
Q34204731 | Recognizing pitfalls in virtual screening: a critical review |
Q35185795 | Selective Mycobacterium tuberculosis Shikimate Kinase Inhibitors as Potential Antibacterials |
Q36032595 | Structure- and sequence-based function prediction for non-homologous proteins |
Q51918959 | Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. |
Q28648497 | Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities |
Q51530156 | Towards structure-based protein drug design. |
Q28478426 | Understanding and classifying metabolite space and metabolite-likeness |
Q38750923 | Why network approach can promote a new way of thinking in biology. |
Q46052028 | kNNsim: k-nearest neighbors similarity with genetic algorithm features optimization enhances the prediction of activity classes for small molecules |
Q47418570 | xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles |
Search more.