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A Study of Structure−Carcinogenic Potency Relationship with Artificial Neural Networks. The Using of Descriptors Related to Geometrical and Electronic Structures
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Quantitative structure-activity relationship (QSAR) studies of mutagens and carcinogens
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Estimation of Maximum Tolerated Dose for Long‐Term Bioassays from Acute Lethal Dose and Structure by QSAR
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Chemometrical treatment of electronic structures of 28 flavonoid derivatives
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Structure–Activity Relationships for Carcinogenic Potential
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Machine Learning Methods in Computational Toxicology
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QSAR modelling of carcinogenicity by balance of correlations
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Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses
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Chemometrics tools in QSAR/QSPR studies: A historical perspective
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QSARs of aromatic amines: Identification of potent carcinogens
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Artificial neural networks for computer-based molecular design
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Uniform-length molecular descriptors for quantitative structure–property relationships (QSPR) and quantitative structure–activity relationships (QSAR): classification studies and similarity searching
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Comparison of spectrum-like representation of 3D chemical structure with other representations when used for modelling biological activity
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Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling
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Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
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An Atlas of Forecasted Molecular Data. 2. Vibration Frequencies of Main-Group and Transition-Metal Neutral Gas-Phase Diatomic Molecules in the Ground State
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Quantitative Structure−Activity Relationships of Mutagenic and Carcinogenic Aromatic Amines
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A General13C NMR Spectrum Predictor Using Data Mining Techniques
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A Study of Structure-Carcinogenicity Relationship for 86 Compounds from NTP Data Base Using Topological Indices as Descriptors
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Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals
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Alternative QSAR models for selected estradiol and cytochrome P450 ligands: comparison between classical, spectroscopic, CoMFA and GRID/GOLPE methods
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QSARs for the Mutagenicity and Carcinogenicity of the Aromatic Amines
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QSAR Modeling for Quinoxaline Derivatives using Genetic Algorithm and Simulated Annealing Based Feature Selection
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Prediction of Carcinogenicity of Chlorine-containing Organic Compounds by Neural Network.
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QSPR/QSAR Analyses by Means of the CORAL Software
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Fuzzy Filtering: A Mathematical Theory and Applications in Life Science
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