ArticleJournal of chemical information and modeling2018
Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models.
Article in Journal of chemical information and modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment.Computers in biology and medicine · 2025Article
- MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Molecular Similarity in Predictive Toxicology with a Focus on the q-RASAR Technique.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents.ACS pharmacology & translational science · 2022Article
- Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.bioRxiv : the preprint server for biology · 2022Article
- Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity.Current issues in molecular biology · 2022Article
- Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.Frontiers in chemistry · 2022Article
- Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.Artificial intelligence in the life sciences · 2021Article
- HybridACS pharmacology & translational science · 2021Article
- Machine learning models for classification tasks related to drug safety.Molecular diversity · 2021Review
- Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.Bioscience reports · 2021Review
- Simplex representation of molecular structure as universal QSAR/QSPR tool.Structural chemistry · 2021Review
- Recommender Systems in Antiviral Drug Discovery.ACS omega · 2020Article
- Network-based piecewise linear regression for QSAR modelling.Journal of computer-aided molecular design · 2019Article
- Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR.Molecular informatics · 2019Article
- Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships.Journal of chemical information and modeling · 2018Article
- Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).
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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.