Jacek Haneczok

Identification of potential SARS-CoV-2 inhibitors 1) Delijewski M., Haneczok J. AI drug discovery screening for COVID-19 reveals zafirlukast as a repurposing candidate. Medicine in Drug Discovery, 2021: Vol.9, p.1-7; https://doi.org/10.1016/j.medidd.2020.100077 2) Haneczok J., Delijewski M. Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations. Journal of Biomedical Informatics, 2021: Vol.119, p.1-12; https://doi.org/10.1016/j.jbi.2021.103821 The first study resulted in a drug discovery screening based on a supervised machine learning model, trained on in vitro data encoded in chemical fingerprints, representing particular molecular substructures. The study identified zafirlukast as the best repurposing candidate for COVID-19, which could be potent against COVID-19 both due to its predicted antiviral properties and its ability to attenuate the so called cytokine storm, thus combining two critical mechanisms in one drug. The second study resulted in a series of drug discovery screenings based on supervised ML models operating in different ways on molecular representations, encompassing shallow learning methods based on fixed molecular fingerprints, Graph Convolutional Neural Network (Graph-CNN) with its self-learned molecular representations, as well as ML methods based on combining fixed and Graph-CNN learned representations. This approach revealed a prevalence of sulfonamides and anticancer drugs, as well as identified novel groups of potential drug candidates against COVID-19, corresponding well with the already published research on COVID-19 treatment, as well as provided novel insights on potential antiviral characteristics inferred from in vitro data.

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