Bender, Andreas, 1976-....
Andreas Bender
Bender, Andreas
Bender, A.
VIAF ID: 107924478 (Personal)
Permalink: http://viaf.org/viaf/107924478
Preferred Forms
- 100 0 _ ‡a Andreas Bender
- 200 _ | ‡a Bender ‡b Andreas ‡f 1976-....
- 100 1 _ ‡a Bender, A.
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- 100 1 _ ‡a Bender, Andreas, ‡d 1976-
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- 100 1 _ ‡a Bender, Andreas, ‡d 1976-....
4xx's: Alternate Name Forms (6)
Works
Title | Sources |
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1, 2010 | |
Applications of proteochemometrics (PCM) : from species extrapolation to cell-line sensitivity modelling | |
Computational approaches in cheminformatics and bioinformatics | |
Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts | |
Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application to Xenopus laevis Phenotypic Readouts | |
Fragment-Based Drug Discovery of Phosphodiesterase Inhibitors. | |
Fragment Profiling Approach to Inhibitors of the Orphan M. tuberculosis P450 CYP144A1. | |
From in silico target prediction to multi-target drug design: current databases, methods and applications | |
General melting point prediction based on a diverse compound data set and artificial neural networks | |
Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes | |
Handbook of chemoinformatics algorithms | |
Harvesting chemical information from the Internet using a distributed approach: ChemXtreme | |
How Consistent are Publicly Reported Cytotoxicity Data? Large-Scale Statistical Analysis of the Concordance of Public Independent Cytotoxicity Measurements | |
How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space | |
Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening. | |
Identification of Novel Class of Triazolo-Thiadiazoles as Potent Inhibitors of Human Heparanase and their Anticancer Activity | |
Identifying novel adenosine receptor ligands by simultaneous proteochemometric modeling of rat and human bioactivity data | |
Improved Chemical Structure-Activity Modeling Through Data Augmentation. | |
Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel | |
In silico target prediction: identification of on- and off-targets for crop protection agents | |
In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window | |
Increased diversity of libraries from libraries: chemoinformatic analysis of bis-diazacyclic libraries | |
Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity | |
Innovation in Small-Molecule-Druggable Chemical Space: Where are the Initial Modulators of New Targets Published? | |
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection | |
Integrating high-content screening and ligand-target prediction to identify mechanism of action | |
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images | |
Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity. | |
Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics | |
Linking Ayurveda and Western medicine by integrative analysis | |
Maximizing gain in high-throughput screening using conformal prediction. | |
Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization | |
Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling | |
Microwave-assisted synthesis, characterization and cytotoxic studies of novel estrogen receptor α ligands towards human breast cancer cells | |
Mining protein dynamics from sets of crystal structures using "consensus structures". | |
Modeling promiscuity based on in vitro safety pharmacology profiling data | |
Modelling compound cytotoxicity using conformal prediction and PubChem HTS data | |
Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features | |
Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives | |
Molecular dynamics simulations and docking of non-nucleoside reverse transcriptase inhibitors (NNRTIs): a possible approach to personalized HIV treatment. | |
Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier | |
Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D). | |
Multi-objective evolutionary design of adenosine receptor ligands. | |
New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets | |
Novel benzoxazine-based aglycones block glucose uptake in vivo by inhibiting glycosidases | |
Novel synthetic coumarins that targets NF-κB in Hepatocellular carcinoma | |
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information | |
P-glycoprotein substrate models using support vector machines based on a comprehensive data set. | |
Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity | |
Polypharmacological in Silico Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat. | |
Predicting the binding type of compounds on the 4 adenosine receptors using proteochemometric models | |
Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure | |
Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling | |
Properties and prediction of mitochondrial transit peptides from Plasmodium falciparum | |
A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design | |
Prospective validation of a comprehensive in silico hERG model and its applications to commercial compound and drug databases | |
Proteochemometric modeling in a Bayesian framework | |
Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules | |
QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping | |
Recognizing pitfalls in virtual screening: a critical review | |
Relating GPCRs pharmacological space based on ligands chemical similarities. | |
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout | |
Revised classification of kinases based on bioactivity data: the importance of data density and choice of visualization. | |
Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets | |
Screening of quinoline, 1,3-benzoxazine, and 1,3-oxazine-based small molecules against isolated methionyl-tRNA synthetase and A549 and HCT116 cancer cells including an in silico binding mode analysis | |
Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data | |
Simultaneous Prediction of four ATP-binding Cassette Transporters' Substrates Using Multi-label QSAR. | |
Studies on Molecular Similarity | |
Substructure-based virtual screening for adenosine A2A receptor ligands | |
Substructure mining of GPCR ligands reveals activity-class specific functional groups in an unbiased manner | |
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. | |
Synergy Maps: exploring compound combinations using network-based visualization | |
Synthesis and in vitro evaluation of hydrazinyl phthalazines against malaria parasite, Plasmodium falciparum | |
Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports | |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria. | |
Target prediction utilising negative bioactivity data covering large chemical space | |
Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis | |
Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling | |
Trisubstituted-Imidazoles Induce Apoptosis in Human Breast Cancer Cells by Targeting the Oncogenic PI3K/Akt/mTOR Signaling Pathway | |
Understanding and classifying metabolite space and metabolite-likeness | |
Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods | |
Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection | |
Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data | |
Untersuchungen an Seitenkanalverdichtern im Hinblick auf den Einsatz als Spülgebläse | |
Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data | |
Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model. | |
Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. | |
Which aspects of HTS are empirically correlated with downstream success? | |
Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development |