Researchers at the University of Cambridge in the UK have significantly accelerated the development of treatment methods for Parkinson's disease using artificial intelligence (AI) technology. They designed and used an AI based strategy to identify blockages α- Synuclein (a characteristic protein of Parkinson's disease) aggregates small molecules. The research results are published in the new issue of Nature Chemical Biology. Parkinson's disease affects over 6 million people worldwide, and it is expected that this number will double by 2040. When people suffer from Parkinson's disease, some proteins lose control and lead to nerve cell death. When these proteins misfold, they can form abnormal aggregates called Lewis bodies, which accumulate in brain cells and prevent their normal operation. One way to find potential treatments for Parkinson's disease is to identify inhibitors that can inhibit α- Small molecules that aggregate synaptic nucleoproteins. This time, the team used machine learning technology to quickly screen a chemical library containing millions of entries to identify small molecules that bind to amyloid protein aggregates and prevent their proliferation. Five highly effective compounds were ultimately identified. Using AI technology, researchers have accelerated the initial screening process tenfold and reduced costs to one thousandth, which means the development of potential therapies for Parkinson's disease is much faster. Using this method, the research team developed a compound to target the "pockets" on the surface of aggregates, which are the cause of exponential growth of the aggregates themselves. The efficacy of this compound is hundreds of times higher than previously reported, and the development cost is much lower. Professor Michel Wendruskolo, who led this study, stated that machine learning is having an impact on the drug discovery process, accelerating the process of identifying the most promising candidate drugs. Due to a significant reduction in time and cost, multiple drug development plans can be implemented in the future. The application of artificial intelligence technology in the field of pharmaceutical research and development is increasingly deepening. The specific manifestation is that the application in this field is not only becoming more and more widespread, but many sub fields of research and development design are also benefiting more from this technology. The use of artificial intelligence technology to design Parkinson's disease drugs greatly improves efficiency, which is a good example. Looking forward to future artificial intelligence technology bringing more new possibilities to drug development. (Lai Xin She)