ArticleJournal of chemical information and modeling2015
QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Assessing the impact of data harmonization on human liver microsomal stability prediction model performance.Results in chemistry · 2026Article
- System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson's disease: a network pharmacology and in-silico evaluation.Scientific reports · 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
- Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets.Chemical research in toxicology · 2023Article
- Integrating Incompatible Assay Data Sets with Deep Preference Learning.ACS medicinal chemistry letters · 2022Article
- Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration.ACS omega · 2021Article
- Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.Scientific reports · 2021Article
- A Chemographic Audit of anti-Coronavirus Structure-activity Information from Public Databases (ChEMBL).Molecular informatics · 2020Article
- QSAR without borders.Chemical Society reviews · 2020Review
- A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy.International journal of molecular sciences · 2020Article
- Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies.Frontiers in genetics · 2020Article
- (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.Molecules (Basel, Switzerland) · 2019Article
- Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.Journal of chemical information and modeling · 2019Article
- HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data.Molecules (Basel, Switzerland) · 2018Review
- Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors.Memorias do Instituto Oswaldo Cruz · 2015Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.
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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.