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Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance
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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 RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS
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Highly discriminating distance-based topological index
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Molecular Connectivity in Chemistry and Drug Research
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Prediction of Peptide Ion Collision Cross Sections from Topological Molecular Structure and Amino Acid Parameters
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Studies of Chemical Structure-Biological Activity Relations Using Pattern Recognition
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Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing
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Atom pairs as molecular features in structure-activity studies: definition and applications
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Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues
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Structure−Activity Relationships of the Antimalarial Agent Artemisinin. 6. The Development of Predictive In Vitro Potency Models Using CoMFA and HQSAR Methodologies
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Evolutionary Optimization in Quantitative Structure−Activity Relationship: An Application of Genetic Neural Networks
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Generalized Analytic Rule Extraction for feedforward neural networks
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Rule extraction from linear combinations of DIMLP neural networks
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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QSAR modelling of integrin antagonists using enhanced Bayesian regularised genetic neural networks
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A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds
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