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Introduction of a Methodology for Visualization and Graphical Interpretation of Bayesian Classification Models

https://doi.org/10.1021/ci500410g
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What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4471-0285-4openalex-snapshot
Pattern Classification
inferredcites →https://doi.org/10.1016/j.bmc.2012.03.030openalex-snapshot
Chemoinformatics: A view of the field and current trends in method development
inferredcites →https://doi.org/10.1016/j.drudis.2010.06.004openalex-snapshot
Data structures and computational tools for the extraction of SAR information from large compound sets
inferredcites →https://doi.org/10.1017/cbo9780511996504openalex-snapshot
Computer Vision
inferredcites →https://doi.org/10.1017/s0033583506004240openalex-snapshot
Designing ligands to bind proteins
inferredcites →https://doi.org/10.1021/ci0003911openalex-snapshot
Algorithm for Naming Molecular Equivalence Classes Represented by Labeled Pseudographs
inferredcites →https://doi.org/10.1021/ci0497861openalex-snapshot
Application of Machine Learning To Improve the Results of High-Throughput Docking Against the HIV-1 Protease
inferredcites →https://doi.org/10.1021/ci050348jopenalex-snapshot
New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching
inferredcites →https://doi.org/10.1021/ci050374hopenalex-snapshot
Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers
inferredcites →https://doi.org/10.1021/ci060003gopenalex-snapshot
Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases
inferredcites →https://doi.org/10.1021/ci0601315openalex-snapshot
Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction
inferredcites →https://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferredcites →https://doi.org/10.1021/ci300306aopenalex-snapshot
Prediction of Activity Cliffs Using Support Vector Machines
inferredcites →https://doi.org/10.1021/ci800079xopenalex-snapshot
Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics
inferredcites →https://doi.org/10.1021/ci900419kopenalex-snapshot
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
inferredcites →https://doi.org/10.1021/jm0303195openalex-snapshot
Classification of Kinase Inhibitors Using a Bayesian Model
inferredcites →https://doi.org/10.1021/jm030363kopenalex-snapshot
Finding More Needles in the Haystack: A Simple and Efficient Method for Improving High-Throughput Docking Results
inferredcites →https://doi.org/10.1021/jm049611iopenalex-snapshot
Molecular Surface Point Environments for Virtual Screening and the Elucidation of Binding Patterns (MOLPRINT 3D)
inferredcites →https://doi.org/10.1021/jm049970dopenalex-snapshot
Combination of a Naive Bayes Classifier with Consensus Scoring Improves Enrichment of High-Throughput Docking Results
inferredcites →https://doi.org/10.1021/jm050180topenalex-snapshot
A Naive Bayes Classifier for Prediction of Multidrug Resistance Reversal Activity on the Basis of Atom Typing
inferredcites →https://doi.org/10.1021/jm100933wopenalex-snapshot
Activity Landscape Representations for Structure−Activity Relationship Analysis
inferredcites →https://doi.org/10.1021/jm3014508openalex-snapshot
Assessing the Target Differentiation Potential of Imidazole-Based Protein Kinase Inhibitors
inferredcites →https://doi.org/10.1021/jm401309qopenalex-snapshot
Efficient Search of Chemical Space: Navigating from Fragments to Structurally Diverse Chemotypes
inferredcites →https://doi.org/10.1021/jm701399fopenalex-snapshot
Chemical Fragments as Foundations for Understanding Target Space and Activity Prediction
inferredcites →https://doi.org/10.1021/jm901137jopenalex-snapshot
New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1039/c1ra00924aopenalex-snapshot
Methods for SAR visualization
inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1108/k.2010.06739hae.001openalex-snapshot
Introduction to Machine Learning
inferredcites →https://doi.org/10.1177/1087057103260590openalex-snapshot
Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier
inferredcites →https://doi.org/10.1177/1087057105281365openalex-snapshot
Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up
inferredcites →https://doi.org/10.1186/1758-2946-3-11openalex-snapshot
Interpreting linear support vector machine models with heat map molecule coloring
inferredcites →https://doi.org/10.1186/1758-2946-5-43openalex-snapshot
Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5281/zenodo.11371openalex-snapshot
Visualization and Graphical Interpretation of Bayesian Compound Classification Models

What points at this work

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

inferred← citeshttps://doi.org/10.1007/s10822-020-00314-0openalex-snapshot
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
inferred← citeshttps://doi.org/10.1016/j.ailsci.2021.100009openalex-snapshot
Chemistry-centric explanation of machine learning models
inferred← citeshttps://doi.org/10.1016/j.ailsci.2022.100048openalex-snapshot
Interpretation of multi-task clearance models from molecular images supported by experimental design
inferred← citeshttps://doi.org/10.1016/j.bmc.2014.12.020openalex-snapshot
Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00519openalex-snapshot
Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00144openalex-snapshot
Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00175openalex-snapshot
Visualization and Interpretation of Support Vector Machine Activity Predictions
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01789openalex-snapshot
Explainable Machine Learning for Property Predictions in Compound Optimization
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b01101openalex-snapshot
Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values
inferred← citeshttps://doi.org/10.1038/s41598-021-01099-4openalex-snapshot
Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
inferred← citeshttps://doi.org/10.1136/bmjopen-2024-098476openalex-snapshot
Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population
inferred← citeshttps://doi.org/10.1186/s13321-018-0310-yopenalex-snapshot
Implicit-descriptor ligand-based virtual screening by means of collaborative filtering
inferred← citeshttps://doi.org/10.1371/journal.pone.0164568openalex-snapshot
Explaining Support Vector Machines: A Color Based Nomogram
inferred← citeshttps://doi.org/10.1517/17460441.2016.1117070openalex-snapshot
An overview of molecular fingerprint similarity search in virtual screening
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