Improving quality of ligand-binding site prediction with Bayesian optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10369452" target="_blank" >RIV/00216208:11320/17:10369452 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/BIBM.2017.8218024" target="_blank" >https://doi.org/10.1109/BIBM.2017.8218024</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM.2017.8218024" target="_blank" >10.1109/BIBM.2017.8218024</a>
Alternative languages
Result language
angličtina
Original language name
Improving quality of ligand-binding site prediction with Bayesian optimization
Original language description
Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In general they are either fast and relatively inaccurate (e.g. purely geometric methods) or accurate but too slow for large scale applications (e.g. methods that rely on a large template libraries of known protein-ligand complexes). P2Rank is a recently intorduced machine learning based method that have already exhibited speeds comparable to fastest geometric methods while providing much higher identification success rates. Here we present an improved version that brings speed-up as well as higher quality predictions. A leap in predictive performance was achieved thanks to the technique of Bayesian optimization, which allowed simultaneous optimization of numerous arbitrary parameters of the algorithm. We have evaluated our method with respect to various performance and prediction quality criteria and compared it to other state of the art methods, as well as to it's previous version, with encouraging results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2017 IEEE International Conference on Bioinformatics and Biomedicine
ISBN
978-1-5090-3049-1
ISSN
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e-ISSN
neuvedeno
Number of pages
2
Pages from-to
2278-2279
Publisher name
IEEE (The Institute of Electrical and Electronics Engineers)
Place of publication
Neuveden
Event location
Kansas City, MO, USA
Event date
Nov 13, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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