Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Autor:	Dr. Dominique Bruns, Dr. Daniel Merk, Dr. Karthiga Santhana Kumar, PD Dr. Martin Baumgartner, Prof. Dr. Gisbert Schneider
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	chemoinformatics chemotaxis drug discovery neural networks phenotypic screening Chemistry QD1-999
Zdroj:	ChemistryOpen, Vol 8, Iss 10, Pp 1303-1308 (2019)
Druh dokumentu:	article
ISSN:	2191-1363
DOI:	10.1002/open.201900222
Popis:	Abstract Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7ae3e2e4ed7148e1aa1b6498568ac75c Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.