Hyper Lab Blog

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.

AI Drug Discovery: From 5 Years to Just 3 Months—Discovering a 100nM Active Compound with Hyper Lab

Oncology, immunology, and metabolic diseases dominated the top 50 FDA-approved drugs in 2024, with innovative treatments like Lazertinib and diverse drug types.