AI Drug Discovery
Support Platform

Extraction of many unreported target molecules by utilizing KIBIT
Narrowing down the target molecules to even more precise targets utilizing original analysis methods

KIBIT is our proprietary natural language processing AI engine, designed to discover unreported relationships — associations not documented in literature — through a novel approach called non-continuous discovery. At the Drug Discovery AI Factory, we utilize KIBIT to generate networks that analyze the relationships among molecules associated with a disease. In this process, KIBIT also predicts target molecules even if their relationships to the disease are not reported.
Based on the network, we utilize our own innovative analysis methods, the Drug Discovery Best Known Method (DD-BKM), to narrow down the targets with high potential for new drugs.

Example: Gene network for disease A

: Target molecules that are highly relevant to disease A and whose relevance is not described in a paper

Disiease A Gene B

No hit

Disiease A Gene C

No hit

No linkage to disease A has been reported for both gene B and gene C

Extraction of genes with high probability of drug discovery success

: Target molecules with a high probability of successful drug discovery are selected from among target molecules that are highly relevant to disease A and whose relevance has not been described in papers.

Analysis example

Virtual Experiments with KIBIT

We simulate how pathways are changed by hypothetically knocking out (KO) the target gene.
For example, in the figure below, a hypothetical KO of gene B has almost no effect on the network as it is only replaced by gene D. On the other hand, when gene C is knocked out, the network changes significantly, suggesting that gene C is the gene affecting the disease.

Unique technology to discover unknowns from known information
Predicts even molecules that have no association with disease in literatures

The main source of article data for analysis is PubMed, and Springer Nature article data can be added as an option. FRONTEO's drug discovery support platform is characterized by its ability to extract novel target molecules from previously reported information.

KIBIT can read known article information and derive unreported relationships.

Experts with years of experience in drug discovery research make proposals that meet customer needs

To analyze highly specialized information and derive innovative outputs, drug discovery researchers with expertise in both drug discovery and AI is essential. FRONTEO brings together a team of researchers who have long been engaged in drug discovery at pharmaceutical companies and research institutions to realize useful proposals, including appropriate target molecules and indications and their hypotheses, in line with the needs of our clients.

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  Hiroyoshi Toyoshiba

  Director/CTO
  Doctor of Science

  Graduated from Waseda University Graduate School of Science and Engineering Department of Mathematics. Engaged in research using statistical analysis of various data at the US National Institute of Environmental Health Sciences, and others. Developed AI algorithms for life sciences.

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  Makoto Miyamoto

  Research Team Director
  Doctor of Agriculture

  Graduated from Kyoto University graduate school. At Takeda Pharmaceutical Company Limited, he was engaged extensively in preclinical safety evaluation, special toxicity (phototoxicity) evaluation, elucidation of toxicity mechanisms, and safety biomarker searches from the early to late stages of new drug development.

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  Kazumi Hayashi

  Research Team
  Doctor of Pharmacy

  After receiving his doctorate in pharmacy, he joined Yamanouchi Pharmaceutical (currently Astellas Pharma). As a research manager, he is in charge of inflammation, cardiovascular, and renal areas. He set up a drug repurposing room and engaged in DR research. He is in charge of devising analysis methods using KIBIT, planning analysis policies, analysis, and generating hypotheses.