engleski

(Re)purposing starts virtually by predictive machine-learning models

The cheapest, fastest and efficient way to find active compounds for biological targets, novel biological activities for compounds “in shelves” or to reveal targets for discovered natural products, is to test compounds virtually by using proper in silico approaches and/or models at the beginning. The predictive machine learning (ML) model developed for specific (bio)activity can be applied for prioritizing assays for in vitro testing of compounds and their (re)purposing. The two ML models which may shed light on potential biological activities for compounds, will be presented. They have been developed by using publicly available datasets and are incorporated within virtual modular platforms for detecting potential phytotoxic molecules and kinase inhibitors.

Cheminformatics, Herbicides, Kinases, Machine learning

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