The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Random Forest Models To Predict Aqueous Solubility
Abstract
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.
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Cited by
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584
Machine learning methods in chemoinformatics
Mitchell, J. B. O. (2014) · WIREs Computational Molecular Science · doi:10.1002/wcms.1183 · Cited by 469
Self-citationMaley, S. M., Kwon, D.-H., Rollins, N., Stanley, J. C., Sydora, O. L., Bischof, S. M., & Ess, D. H. (2020) · Chemical Science · doi:10.1039/d0sc03552a · Cited by 81
Sharma, A. K., Srivastava, G. N., Roy, A., & Sharma, V. K. (2017) · Frontiers in Pharmacology · doi:10.3389/fphar.2017.00880 · Cited by 76
Water Distribution Systems Analysis Symposium–Battle of the Attack Detection Algorithms (BATADAL)
Aghashahi, M., Sundararajan, R., Pourahmadi, M., & Banks, M. K. (2017) · World Environmental and Water Resources Congress 2017 · doi:10.1061/9780784480595.010 · Cited by 30
Metadata
- Authors
- Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-12-02
- Volume
- 47
- Issue
- 1
- Pages
- 150-158
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2041392558
- Cited by
- 364
- References
- 28
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
