Israeli and South Korean Companies Collaborate in Drug Innovation with Data Platform

In recent years, dry labs have become the trend in drug development. A dry lab refers to a type of laboratory involving the application of computational analysis with a computer-generated model, it helps scientists save time and resources to speed up the whole research process by screening out the possible candidates, such as the structure of bio-receptors. With the software and huge databases, the simulation of the experiments can be done remotely. 

CytoReason, an Israeli developer of computational disease models, launched its collaboration with a leading Korean company Helixrus, which focused on biological data and multi-omics. CytoReason and Helixrus both provide services to pharmaceutics in drug development. The partnership may imply a greater bio-data platform formed, which boosts pharmaceutical research. 

CytoReason’s AI Platform to Shorten the Trial Duration while Helixrus Targets Asians

CytoReason is an Israeli company concentrated on computational modeling of human diseases, which enables the biopharma companies to use the platform during the drug research and development to shorten the trial duration. It is achieved by gathering the information from countless indications, forming a huge database to tell, for example, which genes are active. 

Those results may help with prioritizing new target biomarkers and simplifying the complex molecules, speeding up the progress of drug development. As a leading company in bio-data, CytoReason collaborates with five of the world’s most prominent drug companies, such as Pfizer, Roche, and Sanofi. 

Meanwhile, Helixrus provides similar services to CytoReason but the differences are that Helixrus does not have an AI platform and they target Asian populations. The advanced biotechnology development in Korea contributes to the growth of bio-databases. The collaboration between CytoReason and Helixrus means the greater database is built. The larger and more varied data means increasing data accuracy and lower data bias, which contributes to medical research and safety issues. 

Written by Pence Au

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