DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemical Information and Modeling

ISSN

1549-9596

e-ISSN

—

Svazek periodika

63

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

3629-3636

Kód UT WoS článku

001021719300001

EID výsledku v databázi Scopus

2-s2.0-85162900422