| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jcisd/ZhuTFVPGODCT08 |
| P356 | DOI | 10.1021/CI700443V |
| P8608 | Fatcat ID | release_fgwjd75mwbg5zneh4atcdqqj6m |
| P698 | PubMed publication ID | 18311912 |
| P5875 | ResearchGate publication ID | 5540255 |
| P50 | author | Alexander Tropsha | Q4720252 |
| Denis Fourches | Q29460345 | ||
| Igor V. Tetko | Q30362344 | ||
| Tomas Oberg | Q43356360 | ||
| Ester Papa | Q56955974 | ||
| P2093 | author name string | Hao Zhu | |
| Alexandre Varnek | |||
| Artem Cherkasov | |||
| Paola Gramatica | |||
| Phuong Dao | |||
| P2860 | cites work | Virtual Computational Chemistry Laboratory – Design and Description | Q27136404 |
| Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52 | Q30066516 | ||
| P433 | issue | 4 | |
| P921 | main subject | Tetrahymena pyriformis | Q3519169 |
| P304 | page(s) | 766-784 | |
| P577 | publication date | 2008-03-01 | |
| P1433 | published in | Journal of Chemical Information and Modeling | Q3007982 |
| P1476 | title | Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis | |
| P478 | volume | 48 |
| Q39395033 | A Novel Strategy of Structural Similarity Based Consensus Modeling |
| Q39551017 | A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions |
| Q38867278 | A Round Trip from Medicinal Chemistry to Predictive Toxicology |
| Q48791690 | A turning point for blood-brain barrier modeling. |
| Q27062762 | Accessing, Using, and Creating Chemical Property Databases for Computational Toxicology Modeling |
| Q54376609 | Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors |
| Q91093377 | Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling |
| Q64963243 | Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling. |
| Q89859100 | Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets |
| Q45985710 | Are Mechanistic and Statistical QSAR Approaches Really Different? MLR Studies on 158 Cycloalkyl-Pyranones. |
| Q28649930 | Best Practices for QSAR Model Development, Validation, and Exploitation |
| Q90096114 | Big Data and Artificial Intelligence Modeling for Drug Discovery |
| Q37901054 | Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements? |
| Q56916421 | ChemInform Abstract: Combinatorial QSAR Modeling of Chemical Toxicants Tested Against Tetrahymena pyriformis |
| Q30490614 | Chembench: A Publicly Accessible, Integrated Cheminformatics Portal |
| Q56511739 | Chemical Space: Missing Pieces in Cheminformatics |
| Q38835541 | Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? |
| Q38508018 | Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species |
| Q38232706 | Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review |
| Q27162585 | Collaborative development of predictive toxicology applications |
| Q45792175 | Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor |
| Q41612278 | Comparison of in silico tools for evaluating rat oral acute toxicity |
| Q38907478 | Computational approaches for innovative antiepileptic drug discovery |
| Q42540572 | Computational toxicology: realizing the promise of the toxicity testing in the 21st century |
| Q28829021 | Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles |
| Q64996755 | DG-GL: Differential geometry-based geometric learning of molecular datasets. |
| Q30697560 | Data set modelability by QSAR |
| Q35170923 | Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers |
| Q59138094 | Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target |
| Q30929535 | Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling |
| Q37105057 | Development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates. |
| Q34820751 | Development of dimethyl sulfoxide solubility models using 163,000 molecules: using a domain applicability metric to select more reliable predictions |
| Q84785573 | Development of multiple QSAR models for consensus predictions and unified mechanistic interpretations of the free-radical scavenging activities of chromone derivatives |
| Q36223175 | Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs |
| Q40399170 | Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening |
| Q33719719 | Estimation of acute oral toxicity in rat using local lazy learning |
| Q51706586 | Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50). |
| Q43841497 | Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction. |
| Q38914231 | Existing and Developing Approaches for QSAR Analysis of Mixtures. |
| Q38687125 | Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. |
| Q34740499 | Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches |
| Q42921015 | Integrative chemical-biological read-across approach for chemical hazard classification |
| Q38850045 | Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches |
| Q64235133 | Machine-learning approach to the design of OSDAs for zeolite beta |
| Q37194304 | Modeling kinetics of subcellular disposition of chemicals. |
| Q33535332 | Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method |
| Q28818850 | Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM) |
| Q56514129 | Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models |
| Q90124105 | New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques |
| Q52478636 | New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists. |
| Q90478844 | Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models |
| Q37855903 | Per aspera ad astra: application of Simplex QSAR approach in antiviral research |
| Q34475745 | Predicting chemical ocular toxicity using a combinatorial QSAR approach |
| Q93166833 | Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database |
| Q45865749 | Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach |
| Q27902343 | Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process |
| Q28830292 | Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data |
| Q98303153 | Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches |
| Q39540764 | QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae. |
| Q104753065 | QSAR Modeling of SARS-CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2 |
| Q56973893 | QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction |
| Q36233484 | QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action |
| Q57019425 | QSAR and Molecular Modeling Approaches for Prediction of Drug Metabolism |
| Q43558205 | QSAR modeling of the blood-brain barrier permeability for diverse organic compounds |
| Q28222668 | QSAR modeling: where have you been? Where are you going to? |
| Q36409883 | QSAR models of human data can enrich or replace LLNA testing for human skin sensitization |
| Q57819212 | QSARINS: A new software for the development, analysis, and validation of QSAR MLR models |
| Q28385022 | Quantitative nanostructure-activity relationship modeling |
| Q33551379 | Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure |
| Q94033661 | SAR and QSAR modeling of a large collection of LD rat acute oral toxicity data |
| Q28658659 | Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge |
| Q87345163 | Structure-activity analysis of harmful algae inhibition by congeneric compounds: case studies of fatty acids and thiazolidinediones |
| Q28834626 | Supporting read-across using biological data |
| Q35447823 | The acceptance of in silico models for REACH: Requirements, barriers, and perspectives |
| Q27902250 | The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS |
| Q28488557 | Transcriptome profiling of Chironomus kiinensis under phenol stress using Solexa sequencing technology |
| Q28748220 | Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research |
| Q35164442 | Tuning HERG out: antitarget QSAR models for drug development |
| Q39537126 | Using Graph Indices for the Analysis and Comparison of Chemical Datasets |
| Q39551479 | Using Local Models to Improve (Q)SAR Predictivity |
| Q41585844 | Using Pareto points for model identification in predictive toxicology |
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