DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F23%3A43928176" target="_blank" >RIV/60461373:22310/23:43928176 - isvavai.cz</a>
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.3c00434" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.jcim.3c00434</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.jcim.3c00434" target="_blank" >10.1021/acs.jcim.3c00434</a>
Alternative languages
Result language
angličtina
Original language name
DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space
Original language description
The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are user-friendly as well as easily customizable. In this application note, we present the new versatile open-source software package DrugEx for multiobjective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures, a variety of scoring tools, and multiobjective optimization methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx. © 2023 The Authors. Published by American Chemical Society.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Chemical Information and Modeling
ISSN
1549-9596
e-ISSN
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Volume of the periodical
63
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
3629-3636
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85162900422