A machine learning method for the identification and characterization of novel COVID-19 drug targets

Abstract

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 (https://guiltytargets-covid.eu/), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning across Multiple Assays

“…Offering the potential to significantly accelerate the identification and classification of promising drug candidates, these tools have emerged as key contributors in the battle against COVID-19, enabling researchers to efficiently analyze complex biological data and optimize experimental efforts through the predictive power of advanced algorithms. 2 , 3 …”