drTarget applies machine learning models on ChEMBL DB to perform a virtual screening with 1.5M molecules, setting an activity score against SARS-CoV infection.
>60k compounds have been considered as active, and all their experimental records in ChEMBL database are analyzed and aggregated at different levels using interaction network graphs, thus enabling identification of potential viral and host protein targets and pathways against coronaviruses.
The predicted and actual activities for the 37k known substances among the 1.5M ChEMBL compounds establish > 4k drugs with potential for repurposing, so as 93 nutraceuticals.
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Molecule – protein/gene – pathways network interactions graph of the 60k predicted active molecules as recorded in ChEMBL experimental database.