Data Set Modelability by QSAR
Open accessAbstract
We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (correct classification rate above 0.7) for a binary data set of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of nearest-neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 data sets, and the threshold of 0.65 was found to separate the nonmodelable and modelable data sets.
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The concept of modelability proposes that predictivity of QSAR models is limited by activity cliffs.
“Assuming this is not an issue, and considering activity and descriptors together, the relatively new concept of modelability41 proposes that predictivity of QSAR models is then limited by activity cliffs.”
From QSAR without borders (2020) · cites this paper as [41]
Metrics that measure the prevalence of activity cliffs in a dataset are good predictors of the modelability of that dataset.
“Metrics that measure the prevalence of activity cliffs in a dataset are good predictors of the modelability of that dataset.41”
From QSAR without borders (2020) · cites this paper as [41]
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QSAR Modeling: Where have you been? Where are you going to?
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- Authors
- Golbraikh, A., Muratov, E., Fourches, D., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Tropsha, Alexander
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-01-08
- Volume
- 54
- Issue
- 1
- Pages
- 1-4
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC3984298
- OpenAlex
- W2007344527
- Cited by
- 152
- References
- 17
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 16 · 2023: 17 · 2022: 14 · 2021: 11 · 2020: 10 · 2019: 10 · 2018: 16 · 2017: 9 · 2016: 7 · 2015: 11 · 2014: 5 · 2013: 1 · 2012: 1
- FWCI
- 8.75
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Ministry of Earth Sciences
- Ministry of Education, India
- National Institutes of Health (5r01gm096967-03)
- National Institute of General Medical Sciences (GM096967, R01GM066940, R01-GM096967, GM66940)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- bioactive compounds, predictive QSAR models, classification accuracy
- MeSH terms
- Models, Chemical, Drug Design, Computational Biology, Quantitative Structure-Activity Relationship, Databases, Chemical
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