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Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models
- Source :
- J Chem Inf Model
- Publication Year :
- 2018
-
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 ).
- Subjects :
- 0301 basic medicine
Chemical descriptors
Quantitative structure–activity relationship
Similarity (geometry)
Databases, Factual
Computer science
General Chemical Engineering
Quantitative Structure-Activity Relationship
Library and Information Sciences
Machine learning
computer.software_genre
01 natural sciences
Models, Biological
Article
03 medical and health sciences
Software
Simple (abstract algebra)
Toxicity Tests
Humans
Internet
business.industry
Skin sensitization
General Chemistry
0104 chemical sciences
Computer Science Applications
010404 medicinal & biomolecular chemistry
030104 developmental biology
Benchmark (computing)
Artificial intelligence
business
Model interpretation
computer
Algorithms
Mutagens
Subjects
Details
- ISSN :
- 1549960X
- Volume :
- 58
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Journal of chemical information and modeling
- Accession number :
- edsair.doi.dedup.....49e1ea952fb723404a60e78a791be711