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  6. 『Drug Discovery AI Factory』

『Drug Discovery AI Factory』

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last updated:Nov 20, 2024

FRONTEO
FRONTEO
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Utilizing KIBIT, we extract a large number of unreported target genes and further narrow them down to more reliable targets using our unique analysis method.

KIBIT generates a network that maps the connections between genes related to diseases. In doing so, it also predicts target genes that are not mentioned in the literature regarding their association with the disease. To extract high-probability targets from these target genes, it utilizes an innovative analysis method called "Drug Discovery Best Known Method (DD-BKM)" to narrow down to targets with a high potential for new drug development.

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DDAIF500x500_2.png

『Drug Discovery AI Factory』

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  • Related Link - https://lifescience.fronteo.com/products/drug-disc…

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basic information

Utilizing our in-house developed AI "KIBIT," we discovered unreported target genes with a high relevance to diseases. We extracted target genes with a high probability of success using our unique analysis method. We created a gene network and generated hypotheses for the unreported target genes.

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Applications/Examples of results

The target papers for analysis are primarily from PubMed, with the option to include papers from Springer Nature. FRONTEO's drug discovery support service is characterized by its ability to extract highly novel target genes from already reported paper information. Analysis incorporating genomic information and OMICS data is also possible.

Detailed information

  • ddaif_img2-2.png

    We virtually knock out (KO) the genes of interest and simulate how the pathways change. For example, in the figure below, knocking out gene B has almost no impact on the network as it is simply replaced by gene D, whereas knocking out gene C results in significant changes to the network, suggesting that gene C is a gene that affects the disease.

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[Free Online Seminar on December 20] Analysis of Side Effects of Epigenetic Drugs Using Natural Language Processing AI - Focusing on Cardiotoxicity of HDAC Inhibitors -

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"Analysis of Side Effects of Epigenetic Drugs Using Natural Language Processing AI - Focusing on Cardiotoxicity of HDAC Inhibitors -" Conventional pharmaceuticals primarily target proteins such as receptors, but in recent years, epigenetic drugs that target molecules related to controlling protein expression "upstream" have been developed. Among these, the already approved epigenetic drug histone deacetylase (HDAC) inhibitors have achieved significant results in the oncology field with a mechanism of action that is unprecedented. However, it has been reported that these drugs can induce cardiotoxicity, and many details of the mechanisms remain unclear. In this webinar, we will report on the application of FRONTEO's uniquely developed natural language processing AI "KIBIT" to analyze the cardiotoxicity mechanisms of approved HDAC inhibitors, which yielded interesting results. *For more details, please refer to the registration page.*

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FRONTEO

FRONTEO

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FRONTEO supports the judgments of experts in various fields who confront social issues day and night through the provision of its proprietary specialized AI, "KIBIT." This creates a starting point for innovation. Our unique natural language processing technology (patented in Japan and the U.S.) enables fast and high-precision analysis without relying on the amount of training data or computing power, unlike general-purpose AI. Additionally, by utilizing our patented technology that maps (visualizes the structure of) the analyzed information, "KIBIT" can directly influence the insights of experts. In recent years, KIBIT's technology has also been applied to hypothesis generation and target exploration in drug discovery. Through KIBIT's unique technology and approach, we are promoting social implementation in the fields of life sciences AI, business intelligence, economic security, and legal tech AI, with the aim of realizing our philosophy of "providing solutions that do not overlook risks and opportunities buried in records, and achieving fairness in the information society."

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