The British journal Nature Biotechnology recently published a paper co authored by researchers from China, Canada, the United Arab Emirates, the United States, and other countries, explaining how the team used generative artificial intelligence tools to assist in the development of original new drugs, successfully achieving cost reduction and efficiency improvement in the early development of a candidate drug, and achieving ideal results in Phase 1 clinical trials. This is a small molecule candidate drug for the treatment of idiopathic pulmonary fibrosis, led by a team led by AI driven biopharmaceutical company Yingsi Intelligence, utilizing the AI drug discovery platform Pharma AI screening targets and designing drugs. Researchers interact with the platform repeatedly, providing positive or negative feedback to the platform, continuously narrowing the scope of potential target and compound molecular structure screening, and optimizing target and compound selection. Zhao Heng, one of the authors of the new research paper and a researcher at Yingsi Intelligence, told reporters that Pharma AI can analyze multiple data and biological pathways to screen for potential targets, and then list molecular structures based on the selected targets. In addition, the platform can also "reverse" screen, list corresponding targets and applicable diseases for known molecular structures, and shorten the new drug development process through strategies such as "old drugs for new use". The first author of the paper, Ren Feng, CEO and Chief Scientific Officer of Yingsi Intelligence, introduced that before this candidate drug entered clinical practice, the research team generated a total of 78 candidate compounds over 18 months. Compared to traditional pharmaceutical methods, drug development efficiency driven by artificial intelligence has significantly improved, while costs have significantly decreased. At present, this candidate drug is undergoing two randomized double-blind controlled phase 2a clinical trials in China and the United States simultaneously, and is expected to become the first innovative drug with targets and compound molecules discovered and successfully developed by artificial intelligence platforms. (Lai Xin She)