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eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk

https://doi.org/10.1021/ci500666m
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What this work says

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

inferredcites →https://doi.org/10.1006/abbi.1999.1351openalex-snapshot
P450 Gene Induction by Structurally Diverse Xenochemicals: Central Role of Nuclear Receptors CAR, PXR, and PPAR
inferredcites →https://doi.org/10.1016/j.drudis.2010.04.003openalex-snapshot
Evolving molecules using multi-objective optimization: applying to ADME/Tox
inferredcites →https://doi.org/10.1016/j.vascn.2013.04.007openalex-snapshot
Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment
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/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci100072zopenalex-snapshot
Pharmaceutical Perspectives of Nonlinear QSAR Strategies
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/ci500084wopenalex-snapshot
Global Quantitative Structure–Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds
inferredcites →https://doi.org/10.1021/ci950274jopenalex-snapshot
Chemical Similarity Using Physiochemical Property Descriptors
inferredcites →https://doi.org/10.1038/nature04710openalex-snapshot
hERG potassium channels and cardiac arrhythmia
inferredcites →https://doi.org/10.1097/ftd.0b013e31815c16f5openalex-snapshot
Clinically Important Drug Interactions Potentially Involving Mechanism-based Inhibition of Cytochrome P450 3A4 and the Role of Therapeutic Drug Monitoring
inferredcites →https://doi.org/10.1124/pr.55.4.2openalex-snapshot
Induction of Drug Metabolism: The Role of Nuclear Receptors
inferredcites →https://doi.org/10.1146/annurev.pharmtox.39.1.1openalex-snapshot
CYTOCHROME P-450 3A4: Regulation and Role in Drug Metabolism
inferredcites →https://doi.org/10.2165/00002018-200225040-00004openalex-snapshot
Safety of Non-Antiarrhythmic Drugs that Prolong the QT Interval or Induce Torsade de Pointes
inferredcites →https://doi.org/10.2165/00003088-199732030-00004openalex-snapshot
Use of In Vitro and In Vivo Data to Estimate the Likelihood of Metabolic Pharmacokinetic Interactions
inferredcites →https://doi.org/10.2165/00003088-200544030-00005openalex-snapshot
Mechanism-Based Inhibition of Cytochrome P450 3A4 by Therapeutic Drugs
inferredcites →https://doi.org/10.2174/138161212799436430openalex-snapshot
Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties
inferredcites →https://doi.org/10.2174/156802610791111506openalex-snapshot
QSAR in the Pharmaceutical Research Setting: QSAR Models for Broad, Large Problems

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/10.1007/978-3-319-56850-8_9openalex-snapshot
QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
inferred← citeshttps://doi.org/10.1007/s10822-016-9993-1openalex-snapshot
The evolution of drug design at Merck Research Laboratories
inferred← citeshttps://doi.org/10.1007/s10822-021-00421-6openalex-snapshot
Simplified, interpretable graph convolutional neural networks for small molecule activity prediction
inferred← citeshttps://doi.org/10.1016/bs.mie.2018.09.023openalex-snapshot
Knowledge-Based Approaches to Off-Target Screening
inferred← citeshttps://doi.org/10.1016/j.drudis.2020.03.003openalex-snapshot
Machine learning models for drug–target interactions: current knowledge and future directions
inferred← citeshttps://doi.org/10.1016/j.drudis.2026.104827openalex-snapshot
ADMET all-inclusive: Accountability for the full end-to-end model life cycle is key to success
inferred← citeshttps://doi.org/10.1021/acs.analchem.0c04850openalex-snapshot
Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01633openalex-snapshot
Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.0c00445openalex-snapshot
Enhancing Chemogenomics with Predictive Pharmacology
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6b01577openalex-snapshot
Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6b01837openalex-snapshot
Sustainable Practices in Medicinal Chemistry Part 2: Green by Design
inferred← citeshttps://doi.org/10.1039/c5tx00406copenalex-snapshot
Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data
inferred← citeshttps://doi.org/10.1208/s12248-017-0124-2openalex-snapshot
Data to Decisions: Creating a Culture of Model-Driven Drug Discovery
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