In recent years, artificial intelligence (AI) has accelerated the development of new drugs and almost participated in the whole process from drug target discovery to clinical trials. During the COVID-19 epidemic, AI was also behind the emergence of many drugs, and the global "Ai pharmaceutical" achieved accelerated growth. COVID-19 has provided more testing opportunities for "Ai pharmaceuticals". During the epidemic, AI technology was used to accelerate the development of covid-19 small molecule drugs, antibody drugs and vaccines, meeting the urgent needs of the pandemic for drugs. In May this year, yingsi intelligent, an AI innovative drug R & D company headquartered in Hong Kong, China, found an oral inhibitor targeting novel coronavirus main protease (3CL) by using its AI drug R & D platform. In addition, the first covid-19 oral drug approved by the U.S. Food and drug administration, the combination of nimatovir / ritonavir of Pfizer, was also discovered with the help of the AI algorithm of the "marenostrum 4" supercomputer. In addition to discovering new drugs, AI has also accelerated the "new use of old drugs". In 2020, the British benevolent artificial intelligence company announced that through its AI platform, it had discovered that a rheumatic drug baretinib in the United States could be used to treat covid-19. "In the early stage of the epidemic, when there was no medicine to cure, AI's reuse of existing drugs eased the crisis and buffered the development of covid-19 vaccines and drugs." Ren Feng, chief executive officer of yingsi intelligent Co., Ltd., told the reporter. AI also helps optimize vaccine development. German biotechnology company is working with AI start-up instadeep to predict the next high-risk variant of novel coronavirus so that its mRNA vaccine platform can predict. Relevant algorithms have identified a variety of variants of interest including Omicron in advance. To help break through the "anti Moore law", at present, AI can be said to have mastered "18 kinds of martial arts" and has been able to empower different stages of drug research and development, including target discovery, new use of old drugs, compound screening, molecular design and optimization, prediction of preclinical experimental results, patient recruitment and grouping, etc. In recent years, due to the influence of factors such as the increasing difficulty of drug approval and marketing, the rising pharmaceutical cost, and homogeneous competition, the traditional pharmaceutical method has fallen into the "anti Moore's law", that is, although pharmaceutical companies have increased their investment for decades, the number of new drugs on the market with an investment of US $1 billion has been reduced by half every nine years. Ren Feng said that AI can help break through the "anti Moore law" and accelerate the key steps of drug R & D, such as target discovery, compound design and generation, clinical trial design and result prediction, so as to shorten the R & D cycle, reduce the R & D cost and improve the success rate. Drug R & D usually takes a long period (10 to 15 years), has a high investment (more than $1 billion to $2 billion), and has a low success rate (less than 10%). Tens of thousands of small molecules need to be tested and screened first, and only a few can be promoted to clinical research. AI can greatly accelerate this process. According to the data of exensia, a British AI pharmaceutical company, AI shortened the time from the project approval to the pre clinical candidate compounds from an average of 4.5 years to about 13.7 months. Pan Lurong, founder and CEO of Yuanyi wisdom, a company specializing in intelligent drug design platform