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Mathematics in Computer Science
Erschienen in: Mathematics in Computer Science 4/2021

16.06.2021

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

verfasst von: Dimitrios Vlachakis, Panayiotis Vlamos

Erschienen in: Mathematics in Computer Science | Ausgabe 4/2021

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Abstract

COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.
Vorheriger Artikel Computing the Topology of Voronoï Diagrams of Parallel Half-Lines
Nächster Artikel On Column-Convex and Convex Carlitz Polyominoes

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Metadaten
Titel
Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents
verfasst von
Dimitrios Vlachakis
Panayiotis Vlamos
Publikationsdatum
16.06.2021
Verlag
Springer International Publishing
Erschienen in
Mathematics in Computer Science / Ausgabe 4/2021
Print ISSN: 1661-8270
Elektronische ISSN: 1661-8289
DOI
https://doi.org/10.1007/s11786-021-00517-0

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