AIP Network Lab

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List of AIP accelerated PRISM research programs

Construction of language and knowledge processing infrastructure for medical text structuring

Sadao Kurohashi (Professor,Graduate School of Informatics,Kyoto University)

This research project constructs language and knowledge processing infrastructure for clinical texts such as case reports and electronic clinical texts, and patient texts on medical SNS sites, which contain lots of non-grammatical and fragmented expressions and have been difficult to utilize so far. Within the project period, we aim at text structuring that contributes to drug discovery for idiopathic pulmonary fibrosis (IPF) and lung cancer. We also aim to make the infrastucture contribute to precision medicine after the project.

Development of tool enzymes for target verification

Koji Tsuda (Professor,Graduate School of Frontier Sciences,The University of Tokyo)

In this project, we develop new methods for designing high-performance enzymes delivering siRNAs using artificial intelligence. By combining experimental design algorithms such as Bayesian optimization and deep learning and E. coli-based synthesis experiments, our method identifies critical amino acids and recommends how they should be altered. We aim to develop siRNA-delivering enzymes that are highly selective and mass-producible.

Development of the innovative drug discovery system using artificial intelligence

Ryuji Hamamoto (Division Chief,Division of Molecular Modification and Cancer Biology,National Cancer Center Research Institute)

We plan to construct the integrated lung cancer database of the world's largest scale for AI analysis that stores the multiomics data including genome, transcriptome, epigenome and the corresponding clinical information including pathology information, radiation image information, treatment intervention information and patient information. Additionally, we will analyze the data in the integrated lung cancer database using artificial intelligence technology to identify the molecular target of cancer treatment based on clarifying the molecular mechanism of tumorigenesis.

Search for tool compounds to validate the usefulness of drug target molecules

Yoshihiro Yamanishi (Professor,Department of Bioscience and Bioinformatics,Faculty of Computer Science and Systems Engineering,Kyushu Institute of Technology)

In this study, we develop a method to search for tool compounds that can be used for validation of drug target molecules from an enormous set of compounds including approved drugs and bioactive compounds. We analyze large-scale data such as multi-omics information on compounds and diseases, and develop machine learning algorithms to predict effective tool compounds. Finally, we try to discover tool compounds that control candidate molecules for drug targets of idiopathic pulmonary fibrosis and lung cancer, and experimentally evaluate the usefulness of the target candidate molecules.

Exercise-induced AI Infrastructure for Health Saving

Yuta Sugiura (Assistant Professor,Faculty of Science and Technology,Keio University)

”Health Savings” is to store physical abilities which reduces with age through training etc. while they are in a healthy state. With Health Savings, we can expect ”Elongation of healthy life expectancy” that allows us to live a life in an independent and healthy condition by reducing the risk of fall fractures or disease onsets. In this proposal, we aim to build an Information-based technology to collect health savings unconsciously during one’s daily life in order to extend one’s healthy life span and physical abilities to create “Sustainable Human”.