Using Bayesian Modeling on Molecular Fragments Features for Virtual Screening

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10326707" target="_blank" >RIV/00216208:11320/16:10326707 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/CIBCB.2016.7758111" target="_blank" >http://dx.doi.org/10.1109/CIBCB.2016.7758111</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CIBCB.2016.7758111" target="_blank" >10.1109/CIBCB.2016.7758111</a>

Result language

angličtina

Original language name

Using Bayesian Modeling on Molecular Fragments Features for Virtual Screening

Original language description

Virtual screening enables to search large small-molecule compound libraries for active molecules with respect to given macromolecular target. In ligand-based virtual screening, this goal is achieved by utilizing information about fragments or patterns present in existing known active compounds. Typically, the patterns are encoded as fingerprints which are used to screen a database of candidate compounds. In this work, we introduce an approach which uses Bayesian inference to encode activity-related information. Unlike previous approaches, our method does not utilize simple fragments, but rather uses features of these fragments. For each molecule, we generate a set of molecular fragments and extract molecular features for each of them. Next, we remove correlated features and use the remaining ones to build a Bayes model of activity. To score a previously unseen molecule, the molecule's fragment feature vectors are passed to the model and a score is obtained as the aggregation of their probability scores. When screening a database, this score is used to rank the compounds database. We show on datasets with various levels of difficulty that using fragments features rather then fragments themselves results in improvement of retrieval rates with respect to the best state-of-the art molecular fingerprints.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/GP14-29032P" target="_blank" >GP14-29032P: Efficient chemical space exploration using multi-objective optimization</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

ISBN

978-1-4673-9472-7

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

IEEE

Place of publication

Neuveden

Event location

Chiang Mai, Thailand

Event date

Oct 5, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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