Machines Learn Chemistry - Artificial Intelligence Models

Chemists and computer scientists develop artificial intelligence models

Day-to-day life without artificial intelligence is hardly possible in today’s world. Many applications in the field of automatic driving, translating foreign language or medical diagnostics have actually found their way into our lives. Even in chemical researches, excellent efforts are done to apply artificial intelligence. The artificial intelligence is also called machine learning. These modern technologies have currently been used to anticipate the properties of each molecule, making it much easier for scientists to choose the appropriate substance to be produced.

This production, known as synthesis has several possible methods to produce the target molecule due to which it involves considerable effort. Since the success of each individual reaction depends upon various parameters, the anticipation of whether a reaction will take place or not and even less how well it will work is not always possible, even for skilled and experienced chemists. In order to overcome this situation, a group of chemists and computer scientists from the College of Münster, Germany has signed up with forces and developed an artificial intelligence tool, published in the journal Chem.

Background and method

Frederik Sandfort, a Ph.D. student at the Institute of Organic Chemistry as well as one of the lead writers of the publication describes “a chemical reaction as an extremely intricate system.” He also stated, “As opposed to the prediction of properties of specific compounds, a reaction is the interaction of lots of molecules and thus is a multidimensional problem.” Additionally, there are no plainly defined rules of the game, as such in the case of modern chess computers to simplify the growth of artificial intelligence models. For this reason, previous methods to predict reaction results properly such as yields or products are primarily based upon a formerly gained understanding of molecular properties. Finally, Frederik Sandfort said, “The development of such models entails a lot of initiative. Furthermore, most of them are very specialized and also can not be transferred to various other problems.

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The general applicability of the program was the key focus of the presented work so that various other chemists can quickly use it for their own work. To guarantee this, the artificial intelligence model is based directly on molecular structures. Marius Kühnemund, another writer, computer technology, stated, “Every organic compound can be represented as a graph, in principle as an image.” “On such charts, basic structural inquiries similar to the question of colors or shapes in an image can be made in order to capture the so-called chemical environment as accurately as feasible” He added.

A molecular fingerprint is developed with the combination of many such successive queries. These simple number sequences have been used for a long time in chemoinformatics to study structural similarities and also are well fit for computer-aided applications. In their model, the researchers utilize a lot of such molecular fingerprints to represent the chemical structure of each molecule as accurately as feasible. Marius Kühnemund claimed, “By this method, we have actually had the ability to create a durable system that can be made use to anticipate completely different reaction outcomes,” and “The same artificial intelligence model can be used to anticipate both yields as well as stereoselectivities, which is one-of-a-kind.”

The scientists described that their program can be used easily as well as permits accurate predictions, specifically in combination with modern-day robotics, by using a data collection that was not developed for artificial intelligence. “This data collection contains only relative sales of the starting materials and no specific yields,” Frederik Sandfort explains, “Calibrations have to be developed for precise yields. But this is rarely done in real due to the high effort involved.”

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The team will continue to establish their program further as well as equip it with new features in the future. Prof. Frank Glorius is confident – “Computers are fundamentally superior to us when it comes to reviewing huge quantities of complicated data. But our objective is not to change chemists with machines, but the aim is to support them effectively. Artificial intelligence models can significantly transform the way we approach chemical synthesis yet we are still at the beginning.”

Author: Sruthi S

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