CTO Nakayama Contributes Invited Article to Experimental Medicine, Japan’s Leading Life Science Journal, on AI-Driven Drug Discovery

GEXVal Inc.
March 12, 2026

CTO Nakayama Contributes Invited Article to Experimental Medicine Special Issue on AI-Driven Drug Discovery
~Covering the Latest Trends in Data-Driven Drug Discovery and GEXVal's Proprietary Technology~

GEXVal Inc. (President and CEO: Juran Kato, PhD; Location: Fujisawa, Kanagawa, Japan, hereinafter “GEXVal”) is pleased to announce that Chief Technology Officer Yusuke Nakayama, PhD, has been invited to contribute an article to the special issue of Experimental Medicine (Jikken Igaku), published by Yodosha Co., Ltd. on March 5, 2026, under the theme "AI- and Data-Driven Drug Discovery Research."

This special issue brings together leading researchers from academia, research institutions, pharmaceutical companies, national projects, and venture capital — spanning the full ecosystem of AI-driven drug discovery. Dr. Nakayama was invited by the chief editor to contribute a chapter focused on the exploration and optimization of drug candidates.

His article provides an overview of recent technological trends in data-driven drug repurposing — the search for new therapeutic applications of existing drugs and development candidates — including knowledge graphs, graph neural networks (GNN), real-world data analysis, and large language models (LLM). It then details the relationship prediction methodology at the core of RePhaIND^{® 1)}, GEXVal's proprietary next-generation drug discovery platform, using Graph Attention Autoencoder (GATE) ²⁾ . Through a COVID-19 therapeutic discovery case study, the article demonstrates how an orchestration of multi-layered AI analyses enables the systematic identification of novel drug candidates that are difficult to detect through conventional approaches.

Dr. Nakayama, CTO of GEXVal, stated: "Experimental Medicine is a journal I have read since my student days, and I am truly honored to have been given the opportunity to contribute to one of its special issues. This issue brings together leading voices from academia, pharmaceutical companies, and venture capital — all at the forefront of AI-driven drug discovery — making it a work that captures the 'here and now' of this field. I hope that introducing GEXVal's approach within that context will serve as an encouragement as we continue to advance our research and development."

**Publication Details**

[About Experimental Medicine]
Experimental Medicine (Jikken Igaku) is a peer-reviewed specialty journal for researchers in the life sciences and medicine, founded by Yodosha Co., Ltd. in 1983. Its special issues, published eight times a year, are widely used as a resource for tracking research trends, with contributions from leading experts who collectively cover the latest findings in their respective fields.

[About GEXVal]
GEXVal strives to create and develop innovative pharmaceuticals for unmet medical needs, ensuring Treatment Reaches the Unreached with focus on rare diseases and underserved medical conditions. By leveraging our proprietary AI-powered pharmacoinformatics technology, we illuminate paths to breakthrough therapies, identifying hidden potential in drug candidates to deliver life-changing medicines that bring new hope to patients and their families.

1) About RePhaIND^®：Revolutionary Pharmacoinformatics to Find IND (Investigational New Drug)
RePhaIND^® is GEXVal's proprietary AI-driven drug discovery platform with three key features:

• EMPOWER: Discover hidden relationships between drug candidates and diseases
• ACCELERATE: Dramatically improve efficiency compared to conventional methods
• REVOLUTIONIZE: Enable new approaches to unmet medical needs, particularly in rare diseases, bringing a paradigm shift to the drug discovery process

RePhaIND^® is a registered trademark of GEXVal Inc. in Japan, China, Hong Kong, Korea, Australia, Europe, UK, and US and a trademark in other countries and regions.

2) About Graph Attention Autoencoder（GATE）
Graph Neural Networks (GNNs) are AI technologies that learn patterns from various "connections" in data. In particular, Graph Attention Network (GAT) is widely used in everyday services such as social media recommendations and logistics route optimization. Notably, Graph Attention Autoencoder (GATE), which GEXVal pioneered in drug discovery, has previously been limited to specific applications in life sciences, such as protein structure prediction. By innovatively applying GATE technology to the entire drug discovery process, GEXVal enables the discovery of novel therapeutic candidates that would otherwise remain unidentified through conventional methods.

For further information:
Head of Corporate Office
Atsushi Sugizaki
info@gexval.com