ArticleJournal of chemical information and modeling2014
QSAR modeling of imbalanced high-throughput screening data in PubChem.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed.
- Machine learning-driven identification of PIM2 kinase inhibitors through QSAR modeling and molecular dynamics simulations.Journal, genetic engineering & biotechnology · 2026Article
- Machine Learning-Based QSAR Screening of Colombian Medicinal Flora for Potential Antiviral Compounds Against Dengue Virus: An In Silico Drug Discovery Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease.Scientific reports · 2025Article
- Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.Scientific reports · 2025Article
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening.Journal of cheminformatics · 2025Article
- Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning.Journal of cheminformatics · 2025Article
- Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- QSAR Modeling and Biological Testing of Some 15-LOX Inhibitors in a Series of Homo- and Heterocyclic Compounds.Molecules (Basel, Switzerland) · 2024Article
- Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.Journal of chemical information and modeling · 2024Article
- Article
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.ACS medicinal chemistry letters · 2024Article
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- Quantitative Structure-Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity.Molecules (Basel, Switzerland) · 2023Article
- Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.Journal of chemical information and modeling · 2023Review
- Article
- Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.Molecules (Basel, Switzerland) · 2022Article
- Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food-Drug Interactions (FDI).International journal of molecular sciences · 2022Review
- Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents.ACS pharmacology & translational science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many of the structures in PubChem are annotated with activities determined in high-throughput screening (HTS) assays. Because of the nature of these assays, the activity data are typically strongly imbalanced, with a small number of active compounds contrasting with a very large number of inactive compounds. We have used several such imbalanced PubChem HTS assays to test and develop strategies to efficiently build robust QSAR models from imbalanced data sets. Different descriptor types [Quantitative Neighborhoods of Atoms (QNA) and "biological" descriptors] were used to generate a variety of QSAR models in the program GUSAR. The models obtained were compared using external test and validation sets. We also report on our efforts to incorporate the most predictive of our models in the publicly available NCI/CADD Group Web services ( http://cactus.nci.nih.gov/chemical/apps/cap).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.