Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.
OBJECTIVE: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.
METHODS: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.
RESULTS: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.
CONCLUSION: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Current medicinal chemistry - 28(2021), 9 vom: 18., Seite 1746-1756 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bitencourt-Ferreira, Gabriela [VerfasserIn] |
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| Links: |
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| Themen: |
Binding affinity |
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| Anmerkungen: |
Date Completed 18.05.2021 Date Revised 18.05.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/0929867327666200515101820 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM309933358 |
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| 500 | |a Date Revised 18.05.2021 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs | ||
| 520 | |a OBJECTIVE: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes | ||
| 520 | |a METHODS: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions | ||
| 520 | |a RESULTS: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions | ||
| 520 | |a CONCLUSION: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Gibbs free energy | |
| 650 | 4 | |a Machine learning | |
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| 650 | 4 | |a cyclin-dependent kinase | |
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| 700 | 1 | |a de Azevedo Junior, Walter Filgueira |e verfasserin |4 aut | |
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