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Assessing Different Classification Methods for Virtual Screening

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

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Classifying “Kinase Inhibitor‐Likeness” by Using Machine‐Learning Methods
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Extending the trend vector: The trend matrix and sample-based partial least squares
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Discovering novel chemical inhibitors of human cyclophilin A: Virtual screening, synthesis, and bioassay
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Integrating virtual screening in lead discovery
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Computer code for producing Eh-pH plots of equilibrium chemical systems
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QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors
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On the Use of Neural Network Ensembles in QSAR and QSPR
inferredcites →https://doi.org/10.1021/ci025586iopenalex-snapshot
Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs
inferredcites →https://doi.org/10.1021/ci0341565openalex-snapshot
2D QSAR Consensus Prediction for High-Throughput Virtual Screening. An Application to COX-2 Inhibition Modeling and Screening of the NCI Database
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Virtual Screening of Molecular Databases Using a Support Vector Machine
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Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
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Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance
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Classification of Substrates and Inhibitors of P-Glycoprotein Using Unsupervised Machine Learning Approach
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Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning
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Assessing Scoring Functions for Protein−Ligand Interactions
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Virtual Screening of Biogenic Amine-Binding G-Protein Coupled Receptors: Comparative Evaluation of Protein- and Ligand-Based Virtual Screening Protocols
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1042/bst0310603openalex-snapshot
Similarity-based approaches to virtual screening
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Statistical Learning Theory
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BINARY QSAR: A NEW METHOD FOR THE DETERMINATION OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS
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Training v-Support Vector Classifiers: Theory and Algorithms
inferredcites →https://doi.org/10.1177/108705710200700304openalex-snapshot
Comparison of Assay Technologies for a Tyrosine Kinase Assay Generates Different Results in High Throughput Screening
inferredcites →https://doi.org/10.1177/1087057104272660openalex-snapshot
Post-High-Throughput Screening Analysis: An Empirical Compound Prioritization Scheme
inferredcites →https://doi.org/10.2174/1381612053507549openalex-snapshot
New Methodologies for Ligand-Based Virtual Screening
inferredcites →https://doi.org/10.2174/1386207043328706openalex-snapshot
Virtual Screening Methods that Complement HTS
inferredcites →https://doi.org/10.4324/9781003506010-51openalex-snapshot
Personal communication

What points at this work

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

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Virtual Screening
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Progress in the Analysis of Multiple Activity Profile of Screening Data Using Computational Approaches
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Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method
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The influence of training actives/inactives ratio on machine learning performance
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The influence of negative training set size on machine learning-based virtual screening
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The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening
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