Hyper Lab Blog - AI

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.

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 interviewed Prof. Jan Korabecny and Barbora Svobodova, drug discovery researchers at the Biomedical Research Center at the Czech University Hospital, about their experience with Hyper Lab.

Originally developed 30 years ago, the traditional MSA technique has dramatically enhanced AlphaFold’s protein structure prediction accuracy, bringing it back into the spotlight after years of relative obscurity.

Did you know that molecular docking, a key tool in drug discovery, can yield vastly different results depending on how you configure settings like thoroughness, scoring functions, and flexible residues?

Ultra-large compound libraries leverage DEL technology and AI to increase the efficiency of drug discovery, expanding chemical diversity and the potential for hit discovery.