South Korean AI drug discovery firm Standigm has signed a memorandum of understanding with non-profit research institute Institut Pasteur Korea to collaborate in discovering new anti-tubercular drug candidates.

WHAT IT'S ABOUT

This latest partnership is building on the companies' previous research collaboration where they were able to identify "effective" hit compounds with several key scaffolds against multidrug-resistant and extensively drug-resistant tuberculosis.

Based on those identified scaffolds, Standigm will explore new lead structures using its AI platform for compound design. The system will prioritise the most active anti-tubercular compounds and select the final candidates for synthesis. IPK will then turn those virtual candidates into real chemical compounds and evaluate their efficacy. 

WHY IT MATTERS

Tuberculosis is one disease area where innovation in the drug development process is needed, given that costs are not easily recovered, according to Standigm co-founder and CEO Jinhan Kim. "It is not easy to recover drug development costs because it occurs frequently among socially and economically vulnerable groups," he explained. 

Standigm's partnership with IPK, Kim said, will accelerate the discovery of novel tuberculosis drugs by "by reducing the time and cost compared to traditional methodologies". 

"Through this research collaboration with Standigm, we will establish and verify innovative strategies that can accelerate AI-based infectious disease research, and improve the efficiency of drug discovery," IPK CEO Youngmee Jee also said.

Later, Standigm and IPK will conduct follow-up research to commercialise new anti-tubercular drugs, Jee shared. 

MARKET SNAPSHOT

Israeli startup CytoReason has also entered into a collaboration with drug research and development firm Summit Pharmaceuticals International of Sumitomo Corporation in Japan. The companies are working on a client's drug and comparing it with existing products on the market by leveraging CytoReason's AI platform for drug discovery. 

Meanwhile, to speed up the process of drug discovery, researchers from Chinese insurance firm Ping An have developed a deep learning framework for drug discovery called MPG that learns molecular representations from huge volumes of unlabelled molecules.