Published: Jan. 1, 2024

This study presents the development of a machine learning model aimed at identifying patterns in isolated molecules or those present natural products that may have potential to modulate proteins associated with SARS-CoV-2. The achieved metrics (prediction, accuracy, sensitivity) above 86% binary classification active and inactive chemical compounds targeting Covid-19. Utilizing molecular information from Lotus database, was trained tested using docking techniques coronavirus-associated protein. Furthermore, will be made available allow for insertion structural new provided by metabolomic community, aiming identify indicate interaction target advancement represents significant contribution identification drug candidates against SARS-CoV-2, offering an innovative efficient approach screening therapeutic potential. integration community promotes collaboration exchange knowledge, expanding opportunities discovery agents.

Language: Английский