Dr.NOAH publishes an article on AI-based target screening system in SCI-level international journal

  • Date
    2020-06-05 13:31:55
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A research paper titled "Structure- and Similarity-Based Prediction of Protein Targets for Drug-like Compounds," co-authored by a research team led by Professor Seok Cha-ok of Seoul National University and Dr. Noah Biotech, has been published in the SCI-level International Journal of Chemical Information and Modeling.

This paper introduces the technology that, when there is a new drug structure, can quickly screen the proteins that the drug is likely to combine, among the numerous proteins, and effectively predict the target protein. Two approaches are mainly used to predict the bonds between proteins and compounds, each with its own limitations. Ligand-based method relying on similarity to ligands of known interactions are effective only when similar protein–ligand interactions are known. Receptor-based methods predicting protein–ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database.

In this paper, using both approaches, a new technique for efficient and quick screening of target proteins was presented. It is characterized by pre-docking screening before entering the molecular docking phase, which requires a lot of resources for calculation. Structure and property information of compound/protein within the binding site, and structural similarity of compound is used to perform combined pre-docking screening using the AI technology. Through this process, the number of target proteins is reduced, and the final target protein is screened by performing protein-ligand docking. The key here is that the combined pre-docking screening process saves computation and time, while it maintains overall accuracy in prediction. Based on these technologies, Dr. Noah Biotech is confident that it will be able to predict new effects and side effects by predicting targets and off-target proteins more quickly and accurately for new drugs.

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