Hyper Lab Blog - AI

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.

Sunghan Bae from the HITS AI research team won the 2nd AI Drug Discovery Competition, utilizing the MolClr model and HyperLab's binding affinity predictions to overcome data shortages and imbalances.

We interviewed Ryan Kern from the Georgia Institute of technology about the Hyper Lab, and how it integrates experimentation and computation, and has proven to be very useful in optimizing drug candidates.

We interviewed Celine Evbuomwan and Caitlyn Santos, pharmacy students at Midwestern University, about the Hyper Lab. Their positive feedback was that the Hyper Lab is easy to use and well-stocked with tutorials.

In this Hyper Lab interview, José Antonio Costoya, Head of the Molecular Oncology Laboratory at the University of Santiago de Compostela CiMUS, talks about his research and the AI drug discovery platform Hyper Lab.

Here are three features that utilize Hyper Lab. You can prioritize experiments with hyper-binding, get drug structure suggestions with hyper-design, and see the binding structure of drugs and proteins with the 3D/2D viewer.

At the Drug Discovery Conference (DDC) 2024 and American Association of Cancer Research (AACR) 2024, held in April 2024, HITS unveiled Hyper Lab to the world. In this post, we will introduce a research scenario to demonstrate the use cases of Hyper Lab.

Let's take a look at the trends in drug discovery papers accepted at ICLR 2024.