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QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9783527645121.ch1openalex-snapshot
Current Modeling Methods Used in QSAR/QSPR
inferred← citeshttps://doi.org/10.1002/cjoc.200790138openalex-snapshot
Quantitative Structure Property Relations (QSPR) for Predicting Molar Diamagnetic Susceptibilities, χm, of Inorganic Compounds
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Quantitative Structure-Pharmacokinetic Relationships for Drug Distribution Properties by Using General Regression Neural Network
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Quantitative structure‐toxicity relationship: An “in silico study” using electrophilicity and hydrophobicity as descriptors
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QSAR Modeling and QSAR Based Virtual Screening, Complexity and Challenges of Modern
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Molecular Modeling: Considerations for the Design of Pharmaceuticals and Biopharmaceuticals
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Neural Networks in Building QSAR Models
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Developmental Toxicity Prediction
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In Silico QSPR Studies Based on CDFT and IT Descriptors
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Optimum Refrigerant Selection for Low Temperature Engineering
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Phase Behavior of Biomass Components in Supercritical Water
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Neuro-Genetic Hybrid Approach to Predict siRNA Efficacy in Plant Viruses
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QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components
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Quantitative structure–activity relationships of dihydrofolatereductase inhibitors
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Theoretical study of GSK−3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors
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QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network
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Improvement of bioavailability of water insoluble drugs: Estimation of intrinsic bioavailability (Short Communication)
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Modeling, prediction, and analysis of alkyd enamel coating properties via neural computing
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In Silico Predictions of Solubility
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inferred← citeshttps://doi.org/10.1016/j.cattod.2006.09.029openalex-snapshot
Quantitative structure–uptake relationship of metal-organic frameworks as hydrogen storage material
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Activity prediction of hepatitis C virus NS5B polymerase inhibitors of pyridazinone derivatives
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QSAR study of pyrazolo[1,5-a]pyrimidine derivative inhibitors of Chk1
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Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses
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Generalized classifier neural network
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Predicting the acute neurotoxicity of diverse organic solvents using probabilistic neural networks based QSTR modeling approaches
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From Classification to Regression Multitasking QSAR Modeling Using a Novel Modular Neural Network: Simultaneous Prediction of Anticonvulsant Activity and Neurotoxicity of Succinimides
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Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions
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Comparative Study of Machine-Learning and Chemometric Tools for Analysis of In-Vivo High-Throughput Screening Data
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QSTR Modeling for Qualitative and Quantitative Toxicity Predictions of Diverse Chemical Pesticides in Honey Bee for Regulatory Purposes
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Probabilistic Approaches in Activity Prediction
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Computational approaches for the prediction of the selective uptake of magnetofluorescent nanoparticles into human cells
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QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method
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Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
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Optimized Laplacian Generalized Classifier Neural Network
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Hydrophobicity versus electrophilicity: A new protocol toward quantitative structure–toxicity relationship
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Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
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Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
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Neural Networks as Robust Tools in Drug Lead Discovery and Development
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Quantitative Structure-Uptake Relationship of Metal-Organic Frameworks as Hydrogen Storage Material
inferred← citeshttps://doi.org/10.2116/analsci.23.337openalex-snapshot
Fluorometric Determination of Mixtures of Quinolones by Means of Partial Least Squares and Neural Networks
inferred← citeshttps://doi.org/10.2174/157340911798260269openalex-snapshot
Review of Computer-Aided Models for Predicting Collagen Stability
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Bridging Chemical and Biological Space: QSAR Probing Using 3D Molecular Descriptors
inferred← citeshttps://doi.org/10.3390/mol2net-1-e011openalex-snapshot
New theoretical model for the study of new β-secretase inhibitors
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