HyperLab Blog

The $500B “Stargate Project” — What’s behind President Trump’s bold move, and what does it really mean?

Introduces the concept of deep learning architecture and compares two representative models: Transformer and Titans.

Here’s why AI drug development is gaining attention, along with its past, present, and future at a glance

Hyper Screening X utilizes RxnFlow to efficiently explore a 7-trillion-compound ultra-large library, identifying innovative drug candidates with low binding energy and over 60% synthetic feasibility.

In this article, we introduce the development background and outstanding performance of PIGNet, which overcomes the limitations of traditional molecular docking by integrating deep learning with physical laws.

Introducing the AI-driven drug discovery research unveiled at ICLR 2025: RxnFlow, DynamicFlow, and NEXT-MOL

DeepSeek AI has recently emerged as a major player in the AI industry, offering cutting-edge models with high performance at a significantly lower cost.

In January 2025, the U.S. Food and Drug Administration (FDA) approved several new drugs targeting various diseases.

Big Tech's AI race is shifting from LLMs to protein design. Google DeepMind and OpenAI are taking different approaches, sparking anticipation for breakthroughs in life sciences and drug development.

We introduce a case study on utilizing Hyper Screening to develop a MARK4 inhibitor drug. This article covers the process from identifying initial hit compounds to analyzing experimental results.