The subversion of artificial intelligence in the field of scientific research has just begun.
Artificial intelligence has begun to make great efforts in the field of basic science, and the ideas are believed to bring inspiration to entrepreneurs in the field of artificial intelligence.
Scientists at the University of Liverpool are currently experimenting with machine learning algorithms that simulate an infinite number of combinations between atoms to discover new substances, which makes the computer play the role of the creator to some extent.
In this study, by inputting the composition data of known substances into a machine learning algorithm, the computer can predict the possible combination of atoms in a new substance similar to this, which can help scientists greatly narrow the scope of finding new substances, thereby improving discovery. The efficiency of new materials also allows scientists to focus on the analysis of experimental results and avoid the painful experimental phase as in the past.
In recent years, there have been many cases of artificial intelligence-assisted scientific research, and a large number of startup companies and strong cooperation projects have emerged in this field.
For example, in November 2016, Johnson & Johnson worked with British artificial intelligence company BenevolentAI to evaluate the clinical potential of small molecule compounds using artificial intelligence. Currently, BenevolentAI has obtained a number of new drugs in clinical trials.
A similar case is also in December 2016, pharmaceutical giant Pfizer and IBM: the use of the cloud artificial intelligence platform Watson for Drug Discovery for the development of new anticancer drugs, relying on Watson to analyze a large amount of public data and own data, the results will be Targets for discovering new drugs for immune tumors, researching combination therapies, and selecting patient treatment strategies.
Taking the above-mentioned drug development as an example, the traditional drug development is a process of continuous trial and trial and error. It takes an average of 12 years for the drug to be put into the medicine cabinet from the initial laboratory research, and a drug needs to be invested 66.45 billion. Yuan Renminbi, 7,000,874 hours, 6,587 experiments, 423 researchers.
AI's help in drug development begins with data and generates hypothetical drugs through data processing to more efficiently develop new drugs. Supercomputers can find possible treatments for multiple sclerosis by evaluating 8.2 million compounds in a matter of days.
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