08-Jul-2021 - Eidgenössische Technische Hochschule Zürich (ETH Zürich)

Harnessing AI to discover new drugs inspired by nature

"Virtual chemist" finds molecules that have the same effect as a natural substance but are simpler and low-​cost to produce

Artificial intelligence (AI) is able to recognise the biological activity of natural products in a targeted manner, as researchers at ETH Zurich have demonstrated. Moreover, AI helps to find molecules that have the same effect as a natural substance but are easier to manufacture. This opens up huge possibilities for drug discovery, which also have potential to rewrite the rulebook for pharmaceutical research.

Nature has a vast store of medicinal substances. “Over 50 percent of all drugs today are inspired by nature,” says Gisbert Schneider, Professor of Computer-​Assisted Drug Design at ETH Zurich. Nevertheless, he is convinced that we have tapped only a fraction of the potential of natural products. Together with his team, he has successfully demonstrated how artificial intelligence (AI) methods can be used in a targeted manner to find new pharmaceutical applications for natural products. Furthermore, AI methods are capable of helping to find alternatives to these compounds that have the same effect but are much easier and therefore cheaper to manufacture. 

Target molecules of natural substances

And so the ETH researchers are paving the way for an important medical advance: we currently have only about 4,000 basically different medicines in total. In contrast, estimates of the number of human proteins reach up to 400,000, each of which could be a target for a drug. There are good reasons for Schneider’s focus on nature in the search for new pharmaceutical agents. “Most natural products are by definition potential active ingredients that have been selected via evolutionary mechanisms,” he says.

Whereas scientists used to trawl collections of natural products on the search for new drugs, Schneider and his team have flipped the script: first, they look for possible target molecules, typically proteins, of natural products so as to identify the pharmacologically relevant compounds. “The chances of finding medically meaningful pairs of active ingredient and target protein are much greater using this method than with conventional screening,” Schneider says.

Tested with a bacterial molecule

The ETH chemists tested their concept with marinopyrrole A, a bacterial molecule that is known to have antibiotic, anti-​inflammatory and anti-​cancer properties. However, there had been limited research into which proteins in the human body the natural substance interacts with to produce these effects.

To find possible target proteins of marinopyrrole A, the researchers used an algorithm they developed themselves. Employing machine learning models, the algorithm compared the pharmacologically interesting parts of marinopyrrole A with the corresponding patterns of known drugs for which the target proteins to which they bind are known. Based on the pattern matches, the researchers were able to identify eight human receptors and enzymes to which the bacterial molecule could bind. These receptors and enzymes are involved, among other things, in inflammation and pain processes and in the immune system.

Laboratory experiments confirmed that marinopyrrole A did in fact generate measurable interactions with most of the predicted proteins. “Our AI method is able to narrow down the protein targets of natural products with a reliability often in excess of 50 percent, which simplifies the search for new pharmaceutically active agents,” Schneider says.

Creating a cheap alternative

But the work of Schneider’s research group was not over. If the findings about the target proteins of marinopyrrole A are to result in a useful treatment in the future, it is necessary to find a molecule that is easy to manufacture. After all, marinopyrrole A – like many other natural substances – has a relatively complicated structure, which makes laboratory synthesis time-​consuming and expensive.

To search for a simpler chemical compound with the same effect, the ETH researchers used yet another algorithm they designed themselves. This AI program was tasked with being a “virtual chemist” and finding molecules that have similar chemical functionalities to the natural model despite having a different structure. According to the constraints of the algorithm, it also had to be possible to make the molecules in a maximum of three synthesis steps, ensuring easy, low-​cost production.

New chemical structures with the same effect

To define the synthesis path, the software had access to a catalogue of over 200 starting materials, 25,000 purchasable chemical building blocks and 58 established reaction schemes. After each reaction step, the program selected as the starting material for the next step the variants that matched marinopyrrole A most closely in terms of functionalities.

In total, the algorithm found 802 suitable molecules, based on 334 different scaffolds. The researchers synthesized the best four in the laboratory and discovered that they actually behaved very similarly to the natural model. They had a comparable effect on seven of the eight target proteins identified by the algorithm.

Subsequently, the researchers investigated the most promising molecule in detail. X-​ray structure analyses showed that the compter-​generated compound binds to the active centre of a target protein in much the same way as known inhibitors of this enzyme. Despite its different structure, then, the molecule found by AI works using the same mechanism.

Effects on pharmaceutical research

“Our work proves that AI algorithms can be employed in a targeted manner to design active ingredients with the same effects as natural substances, but with simpler structures,” Schneider says, adding: “This helps not only to manufacture new drugs, but also places us on the cusp of a potentially fundamental change in medical-​chemical research.” That is to say, the ETH research group’s methods make it possible to find drugs that do the same things as existing drugs but are based on different structures.  This could make it easier in future to design new unpatented molecular structures. There is currently intense debate regarding both the extent to which AI could be used to systematically circumvent patent protection and the possible patenting of molecules designed by “creative” AI. In any case, the pharmaceutical industry will have to adapt its research approach to a new rulebook.

Facts, background information, dossiers

  • natural products
  • artificial intelligence
  • machine-learning

More about ETH Zürich

  • News

    High-precision frequency measurement

    Many scientific experiments require highly precise time measurements with the help of a clearly defined frequency. Now, a new approach allows the direct comparison of frequency measurements in the lab with the atomic clock in Bern, Switzerland. For many scientific experiments, today’s resea ... more

    The Achilles heel of the Coronavirus

    SARS-​CoV-2 is critically dependent on a special mechanism for the production of its proteins. A collaborative team led by a research group at ETH Zurich obtained molecular insights into this process and demonstrated that it can be inhibited by chemical compounds, thereby significantly redu ... more

    Designing better antibody drugs with artificial intelligence

    Machine learning methods help to optimise the development of antibody drugs. This leads to active substances with improved properties, also with regard to tolerability in the body. Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a f ... more

  • q&more articles

    Analysis in picoliter volumes

    Reducing time, costs and human resources: many basic as well as applied analytical and diagnostic challenges can be performed on lab-on-a-chip systems. They enable sample quantities to be reduced, work steps to be automated and completed in parallel, and are ideal for combination with highl ... more

    Investment for the Future

    This is a very particular concern and at the same time the demand placed annually on Dr. Irmgard Werner, who, as a lecturer at the ETH Zurich, supports around 65 pharmacy students in the 5th semester practical training in “pharmaceutical analysis”. With joy and enthusiasm for her subject sh ... more

  • Authors

    Prof. Dr. Petra S. Dittrich

    Petra Dittrich is an Associate Professor in the Department of Biosystems Science and Engineering at ETH Zurich (Switzerland). She studied chemistry at Bielefeld University and the University of Salamanca (Spain). After completing her doctoral studies at the Max Planck Institute for Biophysi ... more

    Dr. Felix Kurth

    Felix Kurth studied bioengineering at the Technical University Dortmund (Germany) and at the Royal Institute of Technology in Stockholm (Sweden). During his PhD studies at ETH Zurich (Switzerland), which he completed in 2015, he developed lab-on-a-chip systems and methods for quantifying me ... more

    Lucas Armbrecht

    Lucas Armbrecht studied microsystems technology at the University of Freiburg (Breisgau, Germany). During his master’s, he focused on sensors & actuators and lab-on-a-chip systems. Since June 2015, he is PhD student in the Bioanalytics Group at ETH Zurich (Switzerland). In his doctoral stud ... more

q&more – the networking platform for quality excellence in lab and process

The q&more concept is to increase the visibility of recent research and innovative solutions, and support the exchange of knowledge. In the broad spectrum of subjects covered, the focus is on achieving maximum quality in highly innovative sectors. As a modern knowledge platform, q&more offers market participants one-of-a-kind networking opportunities. Cutting-edge research is presented by authors of international repute. Attractively presented in a high-quality context, and published in German and English, the original articles introduce new concepts and highlight unconventional solution strategies.

> more about q&more

q&more is supported by: