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Using Ensembles to Classify Compounds for Drug Discovery

https://doi.org/10.1021/ci034129e
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What this work says

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

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Elements of Information Theory
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Ensemble Methods in Machine Learning
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Feature Selection for Knowledge Discovery and Data Mining
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Compass: A shape-based machine learning tool for drug design
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Application of Nearest-Neighbor and Cluster Analyses in Pharmaceutical Lead Discovery
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Prediction of the Normal Boiling Points of Organic Compounds from Molecular Structures with a Computational Neural Network Model
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Automated Pharmacophore Identification for Large Chemical Data Sets
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Random or Rational Design? Evaluation of Diverse Compound Subsets from Chemical Structure Databases
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Molecular Hashkeys: A Novel Method for Molecular Characterization and Its Application for Predicting Important Pharmaceutical Properties of Molecules
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New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications: Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures
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A Rapid Computational Method for Lead Evolution: Description and Application to α1-Adrenergic Antagonists
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The discovery of indicator variables for QSAR using inductive logic programming
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Bagging Predictors
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Better Decisions through Science
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Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
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Statistical Learning Theory
inferredcites →https://doi.org/10.1109/34.58871openalex-snapshot
Neural network ensembles
inferredcites →https://doi.org/10.1109/kdex.1997.629862openalex-snapshot
Feature selection via set cover
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APPLICATION OF PARAMETER OPTIMIZATION TO MOLECULAR COMPARISON PROBLEMS
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Informative Libraries are More Useful than Diverse Ones
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Popular ensemble methods

What points at this work

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

inferred← citeshttps://doi.org/10.1002/chin.200406231openalex-snapshot
Using Ensembles to Classify Compounds for Drug Discovery.
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Methods for Combinatorial and Parallel Library Design
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Molecular similarity and diversity in chemoinformatics: From theory to applications
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Applying data mining techniques to library design, lead generation and lead optimization
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Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity
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The importance of discerning shape in molecular pharmacology
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The Use of Consensus Scoring in Ligand-Based Virtual Screening
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Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A2BAdenosine Receptor Antagonists
inferred← citeshttps://doi.org/10.1021/ci800164uopenalex-snapshot
Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening
inferred← citeshttps://doi.org/10.1021/jm049066lopenalex-snapshot
Conformation Mining: An Algorithm for Finding Biologically Relevant Conformations
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Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors
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A genetic weighted k-means algorithm for clustering gene expression data
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Asymmetric Bagging and Feature Selection for ActivitiesPrediction of Drug Molecules
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Asymmetric bagging and feature selection for activities prediction of drug molecules
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Drug Discovery Trends
inferred← citeshttps://doi.org/10.1504/ijcbdd.2008.022206openalex-snapshot
Feature selection for the imbalanced QSAR problems by using EasyEnsemble
inferred← citeshttps://doi.org/10.3109/9781420006629-4openalex-snapshot
Drug Discovery Trends
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Unbalance Quantitative Structure Activity Relationship Problem Reduction in Drug Design
inferred← citeshttps://doi.org/10.5740/jaoacint.sge_goodarziopenalex-snapshot
Feature Selection Methods in QSAR Studies
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