HomeJapanPRISM BioLab Partners With Receptor.AI for AI-Driven Drug Discovery

PRISM BioLab Partners With Receptor.AI for AI-Driven Drug Discovery

PRISM BioLab Partners With Receptor.AI for AI-Driven Drug Discovery

PRISM BioLab Co., Ltd. and Receptor.AI Inc. have signed a drug discovery collaboration agreement to develop a new platform that uses artificial intelligence and advanced science to help discover new medicines.

The partnership will combine PRISM’s PepMetics® chemistry technology, which creates special small molecules that can mimic natural protein structures, with Receptor.AI’s AI-powered discovery engine that helps researchers find promising drug candidates more efficiently.

By combining their technologies, the companies hope to discover new drug candidates for difficult biological targets, such as protein–protein interactions and membrane proteins. The first focus of the collaboration will be on metabolic diseases, including obesity.

“We are very pleased to partner with Receptor.AI on this exciting project,” said Dai Takehara, President & CEO of PRISM BioLab. “We have developed PepMetics® Technology, a highly effective drug discovery platform targeting PPIs, and possess a unique peptide‑mimetic small‑molecule library, the PepMetics® Library. We believe this library can be applied not only for intracellular PPI drug discovery but also for membrane proteins, which are not easily targeted by small molecules. With Receptor.AI’s AI‑based molecular design technologies ranging from peptides to small molecules, we expect the potential of the PepMetics® Library to expand even further.”

“We’re excited to partner with PRISM BioLab to move intracellular and receptor-driven discovery from ‘screening harder’ to navigating smarter,” said Dr. Alan Nafiev, Founder & CEO of Receptor.AI. “PRISM contributes a high-quality, structurally constrained virtual chemical space designed for practical progression. We’re bringing QuorumMap — our chemical space navigation engine — together with structure- and physics-informed modeling and multi-objective optimization to continuously reallocate compute and experimental focus toward the compounds most likely to translate: not just binders, but candidates that balance selectivity with the permeability and stability needed for oral exposure. The goal is a repeatable, decision-driven loop that turns difficult intracellular biology into actionable chemical hypotheses and ranked molecules ready for synthesis and testing.”

Receptor.AI Inc. is a U.S.-based TechBio company that is changing how preclinical drug discovery is done by using advanced generative AI platforms. The company has strong technology and has worked with major pharmaceutical companies such as Ono Pharmaceutical in Japan. This shows the growing shift toward data-driven and AI-based research in biotechnology.

At the center of Receptor.AI’s work is its multi-platform AI ecosystem, which helps researchers discover and improve new medicines faster. This system combines several tools into one platform to solve key challenges in drug discovery.

With experience from more than 40 joint discovery projects, Receptor.AI’s platform helps scientists design small-molecule drugs, peptides, and other therapeutic agents more quickly and accurately. Because its tools easily fit into existing research processes, the company has become a trusted partner for large pharmaceutical companies and academic research institutions around the world.

PRISM BioLab Co., Ltd. is a biotechnology company that focuses on discovering and developing new medicines using its special technology called PepMetics®.

The company uses this technology to create small molecule drugs that can be taken orally and that target protein–protein interactions (PPIs). These interactions are important in many diseases, including cancer, autoimmune disorders, and fibrosis.

PepMetics® are specially designed molecules that copy the 3D shapes of certain protein structures, such as alpha-helix and beta-turn. These structures are often involved in how proteins interact inside the body.

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