Now the most promising materials for biocompatible electronics are π-conjugated oligopeptides – a family of self-organizing peptides, which consists of several thousand types of substances.
To determine the correct molecular sequences when creating optimal self-organizing nanostructures, scientists have to consider each of them separately – while studying a single position in the laboratory lasts at least a month.
In order to speed up the search for candidates for the role of optimal self-organizing nanostructures in this family of peptides, the researchers created a machine-learning algorithm created on the basis of Beys organization.
AI analyzed 8,000 potential peptides and selected 186 of the most suitable π-conjugated oligopeptides. Then the researchers checked the results of the algorithm using molecular modeling and reduced the list to a few dozen candidates.
Now, scientists analyze selected π-conjugated oligopeptides using AI, and then plan to re-check the results of its work. In the future, selected compounds can be used to create biocompatible electronics – for example, implants or pacemakers – that do not provoke an aggressive reaction from the immune system.