CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network
Abstract: A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network ; volume:7 ; number:1 ; year:2019 ; pages:121-134 ; extent:14
Computational and mathematical biophysics ; 7, Heft 1 (2019), 121-134 (gesamt 14)
- Creator
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Syrlybaeva, Raulia R.
Talipov, Marat R.
- DOI
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10.1515/cmb-2019-0009
- URN
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urn:nbn:de:101:1-2410261658390.277704832967
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:37 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Syrlybaeva, Raulia R.
- Talipov, Marat R.
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