Artificial intelligence makes scientific studies smarter | Kathmandu Tribune

By Yu Sinan, People’s Daily

The scale of China’s core artificial intelligence (AI) industry has exceeded 400 billion yuan (US$55.2 billion), and the number of related companies is more than 3,000.

Thanks to the booming demand due to massive data processing and the test field of numerous application scenarios, China is now the world leader in computer vision and speech recognition.

The Ministry of Industry and Information Technology, along with other five relevant departments, recently issued a guide on accelerating high-level AI applications to promote high-quality development. It aims to create major application scenarios and expand the application of AI technology, including conducting high-level scientific research.

Today, China is experiencing rapid development in the AI ​​sector and enjoying advantages in data collection, experiment prediction and result analysis. AI technology has been used in a range of disciplines such as life sciences, mathematics, chemistry and space science.

In turn, the rich application scenarios also drive the development of AI technology and make industries smarter.

AI is widely used in life sciences, especially in the prediction of protein structures.

The structures of proteins are three-dimensional. The primary structure of a protein consists of a sequence of a chain of amino acids. The three-dimensional structure determines the functions of a protein in a cell. Many diseases stem from the abnormal structure of important proteins.

Therefore, only by drawing a “three-dimensional map” of important proteins can drug targets be localized, aiding in the development of effective drugs.

Traditionally, scientists observe protein structures through cryo-electron microscopy, X-ray and magnetic resonance imaging, which are time-consuming, expensive and difficult. Therefore, successful observation cases by traditional methods are rare.

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However, amino acid sequencing is much simpler. Predicting protein structure from the primary structure of proteins is exactly what AI is good at.

As the amount of biological data has skyrocketed and AI technology has advanced in recent years, scientists have made predictive models more accurate.

For example, the AI ​​program AlphaFold caused a stir in December 2020 when it dominated a protein structure prediction competition – its predictions were on par with experimentally determined structures.

Researchers are reported to have predicted the structures of over 200 million proteins from around a million species through AI technology, almost all proteins recorded by scientists.

These structures will have a tremendous impact on structural biology and may even trigger a paradigm shift in the life sciences and improve humanity’s understanding of life.

Demand from the biopharmaceutical industry is a key driver driving the application of AI in life science studies. It is known that the number of drugs developed per $1 billion investment has been steadily declining. The development of new drugs is becoming more difficult and takes longer. New methods of biopharmaceutical development are required and expectations of AI are high.

Earlier this year, the National Supercomputing Center of China in Chengdu, Sichuan Province provided theoretical foundations for growing high-yielding and high-quality wheat through a protein prediction model.

Targeted treatment is another area where AI is being used. Through machine learning, AI is able to decipher the human immune system and the complex immune mechanisms of some diseases, helping people to understand diseases and develop medicines and therapies more efficiently and in a more targeted manner.

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Experts said that in order to promote the development of higher-level AI, it is necessary to fully exploit China’s advantages in its rich application scenarios of AI. Building key application scenarios, focusing on high-level scientific research, will promote better development of China’s AI technology and better promote China’s high-quality development.