| Download | - View supplementary information part 1: Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors (XLSX, 4.2 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1007/s10822-019-00253-5 |
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| Author | Search for: Hinge, Vijaya KumarORCID identifier: https://orcid.org/0000-0002-1892-512X; Search for: Roy, DipankarORCID identifier: https://orcid.org/0000-0002-4703-0130; Search for: Kovalenko, Andriy1ORCID identifier: https://orcid.org/0000-0001-5033-4314 |
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| Affiliation | - National Research Council Canada. Nanotechnology
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| Format | Text, Article |
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| Subject | P-glycoprotein (PgP); PgP inhibitors; multidrug resistance (MDR); 3D-RISM-KH; solvation free energy; excess chemical potential; partial molar volume (PMV) |
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| Abstract | Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical–mechanical based three-dimensional reference interaction site model with the Kovalenko–Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds. |
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| Publication date | 2019-11-19 |
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| Publisher | Springer |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRC-NANO-46 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 76fb8e8b-f5cd-4f7e-a847-749629df6298 |
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| Record created | 2021-02-02 |
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| Record modified | 2021-02-02 |
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