Nobel Prizes: From research to application

The Nobel Prize is considered the highest honor that scientists can receive for their work. Founder and namesake Alfred Nobel stipulated in his will that the prize should be awarded "to those who have been of the greatest service to mankind in the past year." It is awarded in the categories of physics, chemistry, physiology or medicine, literature and for peace efforts. 

The Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton. The Nobel Committee honored their "groundbreaking discoveries and inventions that enable machine learning with artificial neural networks". With their contributions back in the 1980s, they paved the way for the increasingly widespread use of artificial intelligence today. 

Hopfield received the Nobel Prize for the development of the network that was later named after him. In 1982, he presented a simple neural network that could store and reconstruct images and other patterns in data. If the Hopfield network was given an incomplete or slightly distorted pattern, it was able to find the stored pattern that was most similar.

The second prize winner, British-born Geoffrey Hinton, built on Hopfield's work. He wondered whether machines could process patterns in a similar way to humans and find their own categories for interpreting information. He used the Hopfield network and ideas from statistical physics, in particular the Boltzmann equation, to develop his method. In 1985, Hinton presented the "Boltzmann machine".

It learns by example and can independently recognize properties in data in order to perform tasks such as identifying image elements. According to the Nobel Prize committee, such machines are often used in larger networks. Today's artificial neural networks are complex and multi-layered, which enables deep learning to process complex information efficiently.

AI-supported systems of this kind are therefore an important tool for accelerating research and using existing data even more efficiently. At Evonik, the data science platform AIChemBuddy helps to view and analyze existing data from different perspectives and to tap into its full potential for research. The "Buddy" can also be used to conduct virtual experiments, on the basis of which experts can efficiently plan further experiments. Last but not least, it can also be used to optimize test series: Based on the data and objectives fed in by the lab managers, the tool recommends the next experiments to be carried out. The tool adapts its suggestions step by step with each new piece of information.

AI also played a prominent role in this year's Nobel Prize for Chemistry: the award went to the US biochemist David Baker and the research duo Demis Hassabis and John Jumper, who work in the UK. They all used AI methods to better understand how proteins work.

This type of molecule acts as hormones, enzymes, antibodies and as building blocks for various tissues. They control and regulate all chemical reactions and thus form nothing less than the basis of life. Despite the large number of proteins and the wide range of functions they fulfill, their structure always follows the same rules. The exact number and sequence of 20 different building blocks, the amino acids, are decisive.

According to the Nobel Committee, Baker achieved the almost impossible: He developed a completely new type of protein. Using a specially developed computer program, Baker began designing new proteins at the end of the 1990s. This is possible because the three-dimensional shape of the proteins is determined by the sequence of amino acids. This means that the amino acid sequence required to obtain this shape can be derived from a given spatial structure. 

While Baker analyzed the path from protein structure to amino acid sequence, the research duo Demis Hassabis and John Jumper, who received the other half of the Nobel Prize in Chemistry this year, took exactly the opposite perspective: How can the correct protein structure be predicted from a given amino acid sequence? They also used AI methods to solve this decades-old mystery. In 2020, the two researchers, who work for the company Google DeepMind, presented the AI model "AlphaFold2", which can be used to predict the structures of practically all 200 million proteins known to date.

Biotechnological methods are becoming increasingly important in the chemical industry. Biotechnology can help to make industrial processes more efficient: For example, by replacing several chemical process steps with one biotechnological process step. Or by using microbes to produce a substance under comparatively mild environmental conditions that previously had to be chemically synthesized at high temperature and high pressure. At the same time, this often benefits the environment, for example because fewer residual materials are produced, energy consumption is reduced or renewable raw materials can be used.

It is crucial that the biocatalysts used for this purpose, i.e. enzymes or entire cells, are adapted as well as possible to their respective tasks. This is precisely where tools such as Alphafold2 help.

Evonik uses biotechnological processes in the production of numerous sustainable products: Corynebacteria are used in Evonik's Biolys process to produce the amino acid L-lysine from sugar, which is important for efficient animal rearing. In Slovenská Ľupča, Evonik produces bio-based rhamnolipids on an industrial scale in the world's first plant. As biosurfactants for hand dishwashing detergents, they offer an exceptional combination of cleaning performance and mildness as well as high compatibility with skin and aquatic organisms. In the Rheticus project, Evonik is developing artificial photosynthesis to produce valuable chemicals from CO₂ and water via electrolysis with the help of bacteria.

Two US researchers were awarded the 2024 Nobel Prize in Physiology or Medicine. Victor Ambros and Gary Ruvkun jointly discovered the microRNA molecule class in 1993 and elucidated its function.

"Their groundbreaking discovery [...] revealed a completely new principle of gene regulation. This turned out to be essential for multicellular organisms, including humans," the Nobel Committee explained its decision.

The same genetic information is present in the nucleus of every cell in the human body, stored in the DNA. Various mechanisms ensure that only the blueprints that are needed are ever realized, so that one cell becomes a nerve cell and the other, for example, a liver cell. Even before Ambros and Ruvkun's work, biologists were aware of so-called transcription factors: these small molecules attached themselves to certain sections of DNA and prevented it from being read and translated into messenger RNA (mRNA). However, the transcription factors alone could not explain the fine control of the processes. 

Ambros and Ruvkun discovered the micro-RNAs in 1993 while investigating the genetic control of nematodes. Two gene lines appeared to have opposite effects on the development of the worm. They realized that one line coded for a micro-RNA that blocked the production of the protein whose blueprint was stored on the second line.

The two researchers were also able to elucidate the exact mechanism: While transcription factors act directly in the cell nucleus, the micro-RNA regulates gene expression outside of it in the cytoplasm. This is where the cell's protein factories, the ribosomes, normally convert the instructions transmitted by messenger RNA into proteins. This is exactly what the micro-RNA prevents: it attaches itself to the messenger RNA and thus prevents proteins from being produced. The base sequence of the micro-RNA exactly matches the section of the messenger RNA to which it attaches.

Initially, the research world assumed that this was an exotic mechanism that only played a role in threadworms. In the years that followed, however, it became clear that it is a universal mechanism. Today, several thousand micro-RNAs are known.

There is still no approved drug that uses micro-RNA as its active principle. However, there are initial tests, for example to treat cancer, cardiovascular or kidney diseases. It is also clear that the right packaging is needed to deliver micro-RNA-based drugs or vaccines to their target. During the coronavirus crisis, it was lipid nanoparticles that encased the life-saving RNA vaccines. Evonik is one of the most important producers of these aids. Another plant capable of producing these lipid nanoparticles is currently being built in the USA for a three-digit million euro sum. The Lipid Innovation Center comes at just the right time to support the development of the next generation of RNA therapeutics.

These examples show how Nobel Prize-winning research leads to practical applications that improve industrial processes, promote sustainable solutions and ultimately deliver the greatest benefits. The link between basic research and industrial application underlines the importance of the Nobel Prizes for progress in science and society.