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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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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Handbook of Molecular Descriptors

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Atom pairs as molecular features in structure-activity studies: definition and applications

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Prediction of Henry's Law Constants by a Quantitative Structure Property Relationship and Neural Networks

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QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks

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Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods

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Support Vector Machines for the Estimation of Aqueous Solubility

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Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

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Discriminant Function Analyses of Liver-Specific Carcinogens

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Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

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A Discussion of Measures of Enrichment in Virtual Screening: Comparing the Information Content of Descriptors with Increasing Levels of Sophistication

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Assessing Different Classification Methods for Virtual Screening

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Contemporary QSAR Classifiers Compared

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Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection

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The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding

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Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle

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Prediction of the Brain−Blood Distribution of a Large Set of Drugs from Structurally Derived Descriptors Using Partial Least-Squares (PLS) Modeling

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Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals

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Efficient training of the backpropagation network by solving a system of stiff ordinary differential equations

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InfoEvolve™

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A System for Induction of Oblique Decision Trees

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Dealing with the Data Deluge: Handling the Multitude of Chemical Biology Data Sources

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Comparison of partial least squares‐discriminant analysis and soft independent modeling of class analogy methods for classification of Saccharomyces cerevisiae cells based on mid‐infrared spectroscopy

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Molecular Similarity: Methods and Performance

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Statistical Methods in Metabolomics

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AI and Big Data for Drug Discovery

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Important Indicators for Predicting Crop Protection Costs

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Virtual High Throughput Screening Using Machine Learning Methods

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Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

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Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding

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Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening

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Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method

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An image based approach for predicting the effects of endocrine disrupting chemicals on human health using deep learning

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Application of machine learning approaches on quantitative structure activity relationships

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GPU-accelerated machine learning techniques enable QSAR modeling of large HTS data

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Big Data and Artificial Intelligence Modeling for Drug Discovery

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Using Machine Learning Methods to Predict Experimental High Throughput Screening Data

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Portable NIR Spectroscopic Application for Coffee Integrity and Detection of Adulteration with Coffee Husk

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