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231225s2020 xx |||||o 00| ||eng c |
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7 |
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|a 10.1002/jcc.26048
|2 doi
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|a pubmed24n1000.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a da Silva, Amauri Duarte
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Taba
|b A Tool to Analyze the Binding Affinity
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| 264 |
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 31.03.2021
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|a Date Revised 31.03.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a © 2019 Wiley Periodicals, Inc.
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|a Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba. © 2019 Wiley Periodicals, Inc
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a binding affinity
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| 650 |
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4 |
|a drug design
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a protein-ligand interactions
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| 650 |
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4 |
|a scoring function
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| 650 |
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7 |
|a Ligands
|2 NLM
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| 650 |
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7 |
|a Proteins
|2 NLM
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| 700 |
1 |
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|a Bitencourt-Ferreira, Gabriela
|e verfasserin
|4 aut
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| 700 |
1 |
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|a de Azevedo, Walter Filgueira
|c Jr
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 41(2020), 1 vom: 05. Jan., Seite 69-73
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
8 |
|g volume:41
|g year:2020
|g number:1
|g day:05
|g month:01
|g pages:69-73
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| 856 |
4 |
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|u http://dx.doi.org/10.1002/jcc.26048
|3 Volltext
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