Science and Technology Platform Program for Advanced Biological Medicine

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Projects

Development of AI design technology for creating high-performance middle molecule drugs

<Project Leader> Tetsuya Kadonosono

School of Life Science & Technology, Tokyo Institute of Technology

Tetsuya Kadonosono

While antibody drugs have potent therapeutic effects, they have complicated structures and large molecular weights, resulting in low tissue permeability, limited applicability, and high manufacturing costs. As an inexpensive alternative to antibody drugs, the development of a peptide drug that can bind targets, be chemically synthesized, and has low molecular weight is desired.
Middle molecule peptides are attracting attention as a next-generation drug discovery modality to replace antibody drugs. They can target various molecules inside and outside cells like low molecular weight drugs, and have high target specificity like antibody drugs, and are thus ideal drugs that embody the advantages of both types. However, although a large number of target-binding peptides have been reported, few peptides thus far have exhibited an affinity comparable to that of an antibody. Subsequently, cases in which a peptide drug has been approved for medical use are very rare. One of the reasons is that effective design techniques have yet to be established.
In this project, we have developed an advanced peptide design technology called “smart design system” that can improve the performance of peptides by machine learning. The smart design system is highly versatile and is expected to be a major breakthrough in middle molecule drug discovery.

Figure 1 Figure 1: Aim of the research
We will develop a scaffold peptide that will serve as a novel modality in drug discovery and an AI design technology for creating high-performance middle molecule drugs. The scaffold peptide has a size that can be chemically synthesized, which is a great advantage over conventional antibody drugs and low molecular weight antibodies in terms of tissue permeability and formulation cost.
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