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Global Quantitative Structure–Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/minf.201000154openalex-snapshot
Using Local Models to Improve (Q)SAR Predictivity
inferredcites →https://doi.org/10.1002/minf.201100107openalex-snapshot
Automated QSAR with a Hierarchy of Global and Local Models
inferredcites →https://doi.org/10.1002/qsar.200860186openalex-snapshot
Global or Local QSAR: Is There a Way Out?
inferredcites →https://doi.org/10.1021/ci00046a002openalex-snapshot
Atom pairs as molecular features in structure-activity studies: definition and applications
inferredcites →https://doi.org/10.1021/ci00054a008openalex-snapshot
Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors
inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci060064eopenalex-snapshot
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
inferredcites →https://doi.org/10.1021/ci060132xopenalex-snapshot
A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models
inferredcites →https://doi.org/10.1021/ci400466ropenalex-snapshot
Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing
inferredcites →https://doi.org/10.1021/ci400482eopenalex-snapshot
Using Random Forest To Model the Domain Applicability of Another Random Forest Model
inferredcites →https://doi.org/10.1021/ci600560mopenalex-snapshot
Three Data Mining Techniques To Improve Lazy Structure−Activity Relationships for Noncongeneric Compounds
inferredcites →https://doi.org/10.1021/ci700016dopenalex-snapshot
Consensus QSAR Models: Do the Benefits Outweigh the Complexity?
inferredcites →https://doi.org/10.1021/ci900471eopenalex-snapshot
Evaluation of Quantitative Structure−Activity Relationship Modeling Strategies: Local and Global Models
inferredcites →https://doi.org/10.1021/ci950274jopenalex-snapshot
Chemical Similarity Using Physiochemical Property Descriptors
inferredcites →https://doi.org/10.1021/ci9803381openalex-snapshot
Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets
inferredcites →https://doi.org/10.1021/mp0700155openalex-snapshot
On-the-Fly Selection of a Training Set for Aqueous Solubility Prediction
inferredcites →https://doi.org/10.1021/mp300466nopenalex-snapshot
Quantitative Structure–Activity Relationship Models That Stand the Test of Time
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cmdc.201700677openalex-snapshot
Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families
inferred← citeshttps://doi.org/10.1002/minf.201600013openalex-snapshot
Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction
inferred← citeshttps://doi.org/10.1002/minf.201900059openalex-snapshot
Investigation of Factors Affecting the Performance of in silico Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_9openalex-snapshot
QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
inferred← citeshttps://doi.org/10.1007/s00204-015-1618-2openalex-snapshot
Toward a unifying strategy for the structure-based prediction of toxicological endpoints
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.12348-0openalex-snapshot
Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1016/j.chroma.2024.465109openalex-snapshot
Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00678openalex-snapshot
Nearest Neighbor Gaussian Process for Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00591openalex-snapshot
Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.2c00962openalex-snapshot
Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c01124openalex-snapshot
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
inferred← citeshttps://doi.org/10.1021/ci500666mopenalex-snapshot
eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk
inferred← citeshttps://doi.org/10.1021/mp5005965openalex-snapshot
Molecular Properties Determining Unbound Intracellular and Extracellular Brain Exposure of CNS Drug Candidates
inferred← citeshttps://doi.org/10.1038/s41467-024-49979-3openalex-snapshot
Application of machine learning models for property prediction to targeted protein degraders
inferred← citeshttps://doi.org/10.1039/c6gc01836jopenalex-snapshot
QSAR models of human data can enrich or replace LLNA testing for human skin sensitization
inferred← citeshttps://doi.org/10.1080/17460441.2018.1496079openalex-snapshot
Challenges with multi-objective QSAR in drug discovery
inferred← citeshttps://doi.org/10.1093/toxsci/kfy111openalex-snapshot
Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity
inferred← citeshttps://doi.org/10.31220/osf.io/zjn29openalex-snapshot
CORRELATIONAL PATCH-CLAMP SPECTROMETRY OF THE ION CHANNELS
inferred← citeshttps://doi.org/10.34133/csbj.0161openalex-snapshot
Automated Large-Scale Extraction of Nanotoxicity Data from the Literature Using Multimodal Large Language Models
inferred← citeshttps://doi.org/10.4018/ijqspr.2021010101openalex-snapshot
The Position of ADME Predictions in Multi-Objective QSAR
inferred← citeshttps://doi.org/10.4155/fmc-2016-0070openalex-snapshot
Automatically Updating Predictive Modeling Workflows Support Decision-Making in Drug Design
inferred← citeshttps://doi.org/10.4155/fmc.15.51openalex-snapshot
Activity and Property Landscape Modeling is at the Interface of Chemoinformatics and Medicinal Chemistry
inferred← citeshttps://doi.org/10.48550/arxiv.2105.08626openalex-snapshot
Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships
inferred← citeshttps://doi.org/10.70675/a6af77b4z321dz4ab5z8b89zb2a887c44ddcopenalex-snapshot
Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods
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