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The Maximum Common Substructure as a Molecular Depiction in a Supervised Classification Context: Experiments in Quantitative Structure/Biodegradability Relationships

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

inferredcites →https://doi.org/10.1002/etc.5620060101openalex-snapshot
BIODEGRADATION DATA EVALUATION FOR STRUCTURE/BIODEGRADABILITY RELATIONS
inferredcites →https://doi.org/10.1002/etc.5620110502openalex-snapshot
PREDICTIVE MODEL FOR AEROBIC BIODEGRADABILITY DEVELOPED FROM A FILE OF EVALUATED BIODEGRADATION DATA
inferredcites →https://doi.org/10.1002/etc.5620180822openalex-snapshot
PREDICTION OF BIODEGRADABILITY FROM CHEMICAL STRUCTURE: MODELING OF READY BIODEGRADATION TEST DATA
inferredcites →https://doi.org/10.1002/spe.4380120103openalex-snapshot
Backtrack search algorithms and the maximal common subgraph problem
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-0-387-84858-7_7openalex-snapshot
Model Assessment and Selection
inferredcites →https://doi.org/10.1007/978-1-4757-2685-5openalex-snapshot
Evaluation Methods in Medical Informatics
inferredcites →https://doi.org/10.1007/978-94-011-5686-8openalex-snapshot
Biodegradability Prediction
inferredcites →https://doi.org/10.1007/978-94-011-5686-8_10openalex-snapshot
Prediction of Biodegradablity from Chemical Structure
inferredcites →https://doi.org/10.1007/978-94-011-5686-8_4openalex-snapshot
The META-CASETOX System
inferredcites →https://doi.org/10.1007/bf02575586openalex-snapshot
A note on the derivation of maximal common subgraphs of two directed or undirected graphs
inferredcites →https://doi.org/10.1016/s0003-2670(00)82080-2openalex-snapshot
Identification of maximal common substructures in structure/activity studies
inferredcites →https://doi.org/10.1016/s0045-6535(98)00543-8openalex-snapshot
Evaluation and application of models for the prediction of ready biodegradability in the MITI-I test
inferredcites →https://doi.org/10.1021/ci00009a019openalex-snapshot
Molecular substructure similarity searching: efficient retrieval in two-dimensional structure databases
inferredcites →https://doi.org/10.1021/ci00010a009openalex-snapshot
Automatic identification of molecular similarity using reduced-graph representation of chemical structure
inferredcites →https://doi.org/10.1021/ci00010a015openalex-snapshot
An algorithm for the multiple common subgraph problem
inferredcites →https://doi.org/10.1021/ci00031a005openalex-snapshot
Use of a maximum common subgraph algorithm in the automatic identification of ostensible bond changes occurring in chemical reactions
inferredcites →https://doi.org/10.1021/ci00031a006openalex-snapshot
Method for generating a chemical reaction index for storage and retrieval of information. Comments
inferredcites →https://doi.org/10.1021/ci010381fopenalex-snapshot
Heuristics for Similarity Searching of Chemical Graphs Using a Maximum Common Edge Subgraph Algorithm
inferredcites →https://doi.org/10.1021/es00052a018openalex-snapshot
Group contribution method for predicting probability and rate of aerobic biodegradation
inferredcites →https://doi.org/10.1023/a:1007601015854openalex-snapshot
Robust Classification for Imprecise Environments
inferredcites →https://doi.org/10.1080/10629369608031702openalex-snapshot
On the Usefulness and Reliability of Existing QSBRs for Risk Assessment and Priority Setting
inferredcites →https://doi.org/10.2116/analsci.3.23openalex-snapshot
Recognition of Largest Common Structural Fragment among a Variety of Chemical Structures

What points at this work

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

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In SilicoMethods for the Analysis of Metabolites and Drug Molecules
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The effect of structural redundancy in validation sets on virtual screening performance
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The Maximum Common Substructure as a Molecular Depiction in a Supervised Classification Context: Experiments in Quantitative Structure/Biodegradability Relationships.
inferred← citeshttps://doi.org/10.1002/qsar.200510009openalex-snapshot
Data and Graph Mining in Chemical Space for ADME and Activity Data Sets
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The Maximum Common Substructure (MCS) Search as a New Tool for SAR and QSAR
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A Direct Algorithm to Find a Largest Common Connected Induced Subgraph of Two Graphs
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Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching
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Scoring function for DNA–drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories
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An investigation into analyzing patents by chemical structure using Thomson’s Derwent World Patent Index codes
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Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water
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Discovery of Potent Parthenolide-Based Antileukemic Agents Enabled by Late-Stage P450-Mediated C—H Functionalization
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In Silico Assessment of Chemical Biodegradability
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MolDiA: A Novel Molecular Diversity Analysis Tool. 1. Principles and Architecture
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Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy
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Comparison of Three Preprocessing Filters Efficiency in Virtual Screening: Identification of New Putative LXRβ Regulators As a Test Case
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Modeling and predicting aquatic aerobic biodegradation – a review from a user's perspective
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Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review
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Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development
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Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME
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Construction of a novel scoring function by using a genetic algorithm for virtual screening of histone deacetylase inhibitors (HDACi)
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Small Molecule Subgraph Detector (SMSD) toolkit
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Evaluation of Molecular Similarity and Molecular Diversity Methods Using Biological Activity Data
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Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels
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