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Citations since 2017
6 Research Items
Aim Rapidly developing AI and machine learning (ML) technologies can expedite therapeutic development and in the time of current pandemic their merits are particularly in focus. The purpose of this study was to explore various ML approaches for molecular property prediction and illustrate their utility for identifying potential SARS-CoV-2 3CLpro in...
Aims: Over the past few years AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process. The purpose of our study was to identify the best repurposing candidates among FDA approv...
In the two recent decades various security authorities around the world acknowledged the importance of exploiting the ever-growing amount of information published on the web on various types of events for early detection of certain threats, situation monitoring and risk analysis. Since the information related to a particular real-world event might...
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.
Fostering innovation with relevance to academic and industrial R&D in pharmacy and medicine.