ArticleEXCLI journal2015
Prediction of aromatase inhibitory activity using the efficient linear method (ELM).
Article in EXCLI journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Employing Machine Learning-Based QSAR for Targeting Zika Virus NS3 Protease: Molecular Insights and Inhibitor Discovery.Pharmaceuticals (Basel, Switzerland) · 2024Article
- A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides.Scientific reports · 2021Article
- Comparing Machine Learning Models for Aromatase (P450 19A1).Environmental science & technology · 2020Article
- Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.Journal of computer-aided molecular design · 2020Article
- New Potent 5α- Reductase and Aromatase Inhibitors Derived from 1,2,3-Triazole Derivative.Molecules (Basel, Switzerland) · 2020Article
- PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.Cells · 2020Article
- iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.International journal of molecular sciences · 2019Article
- Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.International journal of molecular sciences · 2019Article
- ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia.BMC medical informatics and decision making · 2019Article
- ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides.Molecules (Basel, Switzerland) · 2019Article
- Unraveling the bioactivity of anticancer peptides as deduced from machine learning.EXCLI journal · 2018Review
- Towards understanding aromatase inhibitory activity via QSAR modeling.EXCLI journal · 2018Review
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4 authors.
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Abstract
Aromatase inhibition is an effective treatment strategy for breast cancer. Currently, several in silico methods have been developed for the prediction of aromatase inhibitors (AIs) using artificial neural network (ANN) or support vector machine (SVM). In spite of this, there are ample opportunities for further improvements by developing a simple and interpretable quantitative structure-activity relationship (QSAR) method. Herein, an efficient linear method (ELM) is proposed for constructing a highly predictive QSAR model containing a spontaneous feature importance estimator. Briefly, ELM is a linear-based model with optimal parameters derived from genetic algorithm. Results showed that the simple ELM method displayed robust performance with 10-fold cross-validation MCC values of 0.64 and 0.56 for steroidal and non-steroidal AIs, respectively. Comparative analyses with other machine learning methods (i.e. ANN, SVM and decision tree) were also performed. A thorough analysis of informative molecular descriptors for both steroidal and non-steroidal AIs provided insights into the mechanism of action of compounds. Our findings suggest that the shape and polarizability of compounds may govern the inhibitory activity of both steroidal and non-steroidal types whereas the terminal primary C(sp3) functional group and electronegativity may be required for non-steroidal AIs. The R code of the ELM method is available at http://dx.doi.org/10.6084/m9.figshare.1274030.
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