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Ping An Makes Breakthrough in Artificial Intelligence-Driven Drug Research


HONG KONG and SHANGHAI, June 21, 2021 /PRNewswire/ -- Research by Ping An Healthcare Technology Research Institute and Tsinghua University has led to a promising deep learning framework for drug discovery, announced Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318).

The findings were published in "An effective self-supervised framework for learning expressive molecular global representations to drug discovery" in Briefings in Bioinformatics, a peer-reviewed bioinformatics journal. It marks a major technology breakthrough for the Group in the field of AI-driven pharmaceutical research.

Drug discovery can take 10 to 15 years from invention to market. It can take a large number of experiments, with significant costs and high failure rates. Computer-aided drug discovery for molecule design in pre-clinical research helped to improve the process, but traditional methods were still expensive and time consuming. A variety of artificial intelligence technologies have shown superior speed and performance for different aspects of drug discovery, such as molecule drug design, drug-drug interaction and drug-target interaction predictions. However, molecular modeling has been a challenge, due to the limited amount of labelled data for training datasets. 

Graph neural networks (GNN) have emerged as a powerful tool for modeling molecular data. Instead of relying on labelled data, a model can be pre-trained with unlabeled data. Ping An's research proposed a novel deep learning framework, named MPG, that learns molecular representations from large volumes of unlabeled molecules, and a powerful GNN, called MolGNet, for modelling molecular graphs.

Ping An's research also proposed a self-supervised pre-training strategy, named Pairwise Half-graph Discrimination, which was accepted in IJCAI 2021, a top peer-reviewed artificial intelligence conference. The research team found that after pre-training on 11 million unlabeled molecules, MolGNet can capture meaningful patterns of molecules to produce an interpretable representation. The experimental results showed that MPG achieved the state-of-the-art performance on multiple drug discovery tasks. It is an important first step towards graph-level self-supervised learning on large-scale molecule datasets.

The technology is being used by Ping An Shionogi, a joint venture founded by Ping An and Shionogi & Co., Ltd. a Japanese research-driven pharmaceutical company, for research and development of new drugs and drug repurposing.

About Ping An Group

Ping An Insurance (Group) Company of China, Ltd. ("Ping An") is a world-leading technology-powered retail financial services group. With over 220 million retail customers and 611 million internet users, Ping An is one of the largest financial services companies in the world. Ping An focuses on two over-arching domains of activity, "pan financial assets" and "pan health care", covering the provision of financial and health care services through our integrated financial services platform and our ecosystems; in financial services, health care, auto services and smart city services. Our "finance + technology" and "finance + ecosystem" transformation strategies aim to provide customers and internet users with innovative and simple products and services using technology. As China's first joint stock insurance company, Ping An is committed to upholding the highest standards of corporate reporting and corporate governance. The Group is listed on the stock exchanges in Hong Kong and Shanghai. Ping An ranked 6th in the Forbes Global 2000 list in 2021 and ranked 21st in the Fortune Global 500 list in 2020. Ping An also ranked 38th in the 2020 WPP Kantar Millward Brown BrandZTM Top 100 Most Valuable Global Brands list.

For more information, please visit www.group.pingan.com and follow us on LinkedIn - PING AN.

SOURCE Ping An Insurance (Group) Company of China, Ltd.


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News published on 21 june 2021 at 07:31 and distributed by: