DeepDrug Secures Nearly RMB 10 Million in Angel Funding from Qingsong Fund to Tackle High Drug Prices with AI-Powered Solutions Inspired by 'Dying to Survive'

The hit film “Dying to Survive” has brought into the spotlight the controversial issue of purchasing affordable generic drugs from India to save lives. This phenomenon not only reflects the clash between human nature and the intellectual property system at life-and-death junctures, but also directly points to the global challenge of exorbitant pricing for patented drugs—high risks in new drug development and the low success rate of such endeavors drive up the prices of patented medicines.

However, the rise of artificial intelligence technology has brought hope for improving the efficiency of new drug development.

VCBeat (WeChat ID: vcbeat) has learned that Zhiyao Technology recently announced it had secured an angel-round investment of nearly RMB 10 million from Qingsong Fund. Benchmarking its offerings against those of the U.S. unicorn AtomWise, the company is committed to applying artificial intelligence to new drug discovery and development, with the potential to save the pharmaceutical industry hundreds of millions of dollars in R&D costs.

“Dying to Survive” is based on the true story of Lu Yong, a patient with chronic myeloid leukemia who purchased the generic drug “Gleevec” from India for himself and acted as a purchasing agent for fellow patients. In 2014, Lu Yong was released on bail pending trial, and the procuratorial organs filed public prosecution against him for crimes of obstructing credit card management and selling counterfeit drugs. This incident prompted more than 300 fellow patients to jointly sign a petition pleading for leniency on his behalf. In 2015, the procuratorial organs withdrew the charges against Lu Yong.

The film’s public release sparked widespread social discussion. While feeling sympathy and even admiration for Lu Yong, people were more concerned about whether a solution could be found when patients face the dilemma of having to choose between their right to life and intellectual property rights. One of the core reasons behind this issue is the prohibitively high price of patented drugs.

In 2015, Yang Chen, who had just earned his Ph.D. from the Shanghai Institutes for Biological Sciences of the Chinese Academy of Sciences, also embarked on a journey into the Indian pharmaceutical sector. However, unlike Lu Yong, who engaged in the proxy purchase of medications, Yang founded Kangantu. Through medical tourism, he facilitated trips for hepatitis C patients to India, where they received outpatient care and obtained Indian generic versions of “sofosbuvir” (a highly effective anti-hepatitis C drug developed by Gilead Sciences in the United States) before returning to China for treatment. Through this approach, Yang and his team helped nearly 5,000 Chinese patients access affordable and effective Indian generic medications.

Later, shaken by AlphaGo’s victory over the world’s top Go players, Yang Chen began to contemplate how technological advancements could lower the prices of patented drugs, thereby enabling more Chinese patients to access affordable patented medications and sparing them the risks of purchasing drugs through unofficial channels or the hardships of long-distance travel.

In 2017, Dr. Yang Chen, who holds a Ph.D. in Pharmacology, and Dr. Huang Tao, an expert in computer-aided drug design (CADD), joined forces with top-tier researchers from a bioinformatics team at a prestigious Chinese university to co-found Zhiyao Tech (Ningbo Yang’an Pharmaceutical Technology Co., Ltd.). Registered in Ningbo, with its headquarters in Shanghai and laboratories located at the Suzhou Industrial Park of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (SIBCB-Suzhou Park), the company is dedicated to developing artificial intelligence technologies that significantly enhance the efficiency of drug discovery, particularly for application in the R&D of novel antiviral therapeutics.

So, what exactly can artificial intelligence technology bring to drug development? The most intuitive answer is—improving the efficiency of new drug research and development, reducing R&D costs, thereby lowering drug sales prices and saving more patients' lives.

In fact, the production costs of patented drugs such as “Gleevec” and “Sovaldi” are not high; what truly drives their high prices is the enormous investment in research and development.

Latest data show that the average R&D cost incurred by the pharmaceutical industry for each newly approved drug is approximately $2.6 billion, a figure that encompasses not only the direct investment in the successful drug but also the sunk costs of numerous failed projects. Drug development carries high risks; countless pharmaceutical companies, biotechnology firms, and scientists have devoted substantial financial, material, and human resources over decades, often to no avail.

Fortunately, emerging artificial intelligence technologies have the potential to transform this landscape. As a news report in Nature stated, “Artificial intelligence and machine learning are ushering in a new era of rapid, low-cost, and efficient drug discovery.”

It is reported that Zhiyao Technology’s AI products under development cover all stages from drug discovery to clinical trials, aiming to systematically and comprehensively improve the success rate of new drug development by enhancing efficiency across various R&D links, thereby reducing R&D costs.

Notably, Zhiyao Tech has developed KangDock, a virtual drug screening and molecular docking tool. Its prediction accuracy (AUC) on test datasets has reached 93%, nearly 4 percentage points higher than AtomNet, a comparable product from the U.S. unicorn company AtomWise. The application of this technology is expected to save the pharmaceutical industry hundreds of millions of dollars in R&D costs.

Recently, Qingsong Fund has reached an investment agreement with Zhiyao Technology, providing it with an exclusive angel round investment of nearly RMB 10 million. Taihe Capital served as the financial advisor for this transaction.

Huang Tao, CEO of Zhiyao Technology and a veteran with over 10 years of experience in computer-aided drug design (CADD) and drug discovery and development, stated that the company will leverage this financing to strengthen R&D efforts spanning from algorithms to products, deeply explore the potential of artificial intelligence in new drug development, and help the pharmaceutical industry discover new drugs in a faster, better, and more cost-effective manner, thereby benefiting public health.

Su Wei, a partner at Qingsong Fund, stated that in the United States, AI-assisted drug discovery is the sector with the highest number of unicorns, whereas relevant investment targets are extremely scarce in China. “This is because the technical barriers are exceptionally high, involving machine learning, pharmaceutics, medicine, quantum mechanics, and more, with machine learning being the least prohibitive among them. The team at Zhiyao Technology is highly complementary; its three founders hold PhDs in computer-aided drug discovery, pharmacology, and machine learning, respectively, and are top-tier talents in their respective fields. Furthermore, Zhiyao Technology’s product has already demonstrated world-leading capabilities during the compound screening phase.”

Qingsong Fund was founded in June 2012 by Liu Xiaosong, Dong Zhanbin, and Su Wei, three seasoned veterans from the internet and investment sectors. As a venture capital firm primarily dedicated to early-stage investments, it focuses on sectors such as culture and entertainment, education and training, consumption upgrading, and artificial intelligence. As of June 2018, Qingsong Fund had invested in more than 130 companies. Since its establishment, it has consistently been honored with awards including Zero2IPO’s Top 10 Early-Stage Investment Firms/Top 10 Investors in China, and CVSource’s Top 10 Best Angel and Early-Stage Investment Firms/Top 10 Investors in China.