ArticleJournal of cheminformatics2009
Interpretable correlation descriptors for quantitative structure-activity relationships.
Article in Journal of cheminformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling.Journal of cheminformatics · 2015Article
- Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles.Current pharmaceutical design · 2014Review
- Hibiscus sabdariffa L., roselle calyx, from ethnobotany to pharmacology.Journal of experimental pharmacology · 2012Review
- Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg-Marquardt neural network.Advances and applications in bioinformatics and chemistry : AABC · 2010Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundThe topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (J Chem Inf Model 2007, 47: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art.
resultsHere, we illustrate the interpretability of the TMACC descriptors, through the analysis of the QSARs of inhibitors of angiotensin converting enzyme (ACE) and dihydrofolate reductase (DHFR). In the case of the ACE inhibitors, the TMACC interpretation shows features specific to C-domain inhibition, which have not been explicitly identified in previous QSAR studies.
conclusionsThe TMACC interpretation can provide new insight into the structure-activity relationships studied. Freely available, open source software for generating the TMACC descriptors can be downloaded from http://comp.chem.nottingham.ac.uk.
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