Our laboratory uses artificial intelligence to advance research in the following areas.

1. Drug Discovery Research

　We aim to identify high-affinity compounds that can regulate therapeutic target molecules whose involvement in disease has already been validated.

　Traditionally, compounds that bind to therapeutic target molecules have been identified using large-scale compound libraries and high-throughput screening systems. In recent years, AI-based in silico screening has also made significant progress. However, accurately predicting the affinity between proteins and small-molecule compounds remains challenging, with risks of both overestimation and underestimation.

　To address this issue, we use experimentally obtained affinity data, including data from mass spectrometry, to refine AI-based predictions. By iteratively integrating experimental measurements with AI prediction, we aim to establish a cyclic AI-driven drug discovery platform that enables the efficient identification of promising drug candidates.

2. Medical Applications of Spatial Omics

　Spatial omics enables the simultaneous acquisition of molecular and spatial information. Using this approach, we study disease-related factors as well as spatial and structural patterns that are relevant to disease.

　Through this research, we aim to contribute to the discovery of therapeutic target molecules and biomarkers, and to advance the understanding, diagnosis, and treatment of disease.

Students and researchers interested in medical AI and quantum life science are welcome to contact us. Inquiries from undergraduate students are also encouraged.