Artificial intelligence is trained to create medications from scratch
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In order to develop a new drug, pharmacists have to spend a lot of calculations to create the chemical formula of the active substance and, if possible, to anticipate as a future drug will interact with the various systems of the body. One only this process takes a lot of time, not to mention the laboratory and clinical trials. Soon, however, it can be significantly simplified, because it was created by artificial intelligence that can create new drugs from scratch without human intervention.
According to the publication EurekAlert, a new artificial intelligence consists of two interrelated neural networks, named "student" and "teacher." The fact that at the stage of development when the drug is only "invented" that pharmacists choose a specific molecule with certain properties, then "customize" it to get the effect you want to ideally achieve 100% of potential exposure with minimum side effects. In the process of obtaining the required effects in the course of various chemical reactions scientists "add and subtract" some atoms in a molecule. It is these knowledge and owns AI teacher. In this neural network, the downloaded data on more than 1.7 million of biologically active molecules, their properties and the principle of interaction with each other. The pupil learns from a teacher to the skills of developing drugs and then offers the best substances that can be the basis for new drugs.
At the heart of both neural networks is a system ReLeaSE. It is quite a known method of drug development in the pharmaceutical industry. It is aimed at identifying the best types of active substances for the production of medicines. ReLeaSE uses indicators such as melting temperature, exposure to enzymes, and the solubility in water. According to the creators of the AI-pharmacist
"If we compare the process of learning AI with the study of language after the student learns alphabet and basic rules, it can create new "words", that is, molecules. If the new molecule is stable and has the desired effect — the teacher claims it, and if not — reject the development, forcing the student to avoid future "bad" molecules and create "good".
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