The problem of drug design is crucial in biology of aging. It is very likely that when scientists are able to figure out the right combination of interventions in aging processes, the pharmaceutical therapies will differ a lot from one person to another. That is, of course, true for personalized medicine as well. This means pharmaceutical companies would have to figure out the way how to design and manufacture drugs of different properties in large amount. Thus, this new technique is of great value.
Scientists at Ohio State University are taking the trial and error out of drug design by using powerful computers to identify molecular structures that have the highest potential to serve as the basis for new medications.
Most drugs are designed to act on proteins that somehow malfunction in ways that lead to damage and disease in the body. The active ingredient in these medicines is typically a single molecule that can interact with a protein to stop its misbehavior.
Finding such a molecule, however, is not easy. It ideally will be shaped and configured in a way that allows it to bind with a protein on what are known as “hot spots” on the protein surface – and the more hot spots it binds to, the more potential it has to be therapeutic.
Previous methods to identify these molecules have emphasized searching for fragments that can attach to one hot spot at a time. Finding structures that attach to all of the required hot spots is tedious, time-consuming and error-prone.
Ohio State University researchers however, have used computer simulations to identify molecular fragments that attach simultaneously to multiple hot spots on proteins. The technique is a new way to tackle the fragment-based design strategy.
“We use the massive computing power available to us to find only the good fragments and link them together,” said Chenglong Li, assistant professor of medicinal chemistry at Ohio State and senior author of a study detailing this work.
Li likens the molecular fragments to birds flying around in space, looking for food on the landscape – in this case, the protein surface. With this technique, he creates computer programs that allow these birds – or molecular fragments – to find the prime location for food, or the protein hot spots. The algorithm originated from a computation technique called particle swarm optimization.
The important outcomes of this research can help to create a molecular template that would serve as a blueprint for advanced stages of the drug finding process. Medicinal chemists may accumulate synthetic molecules based on these computer models, which may then be examined for their effectiveness against certain diseases. Li has already applied this technique to recognize molecules that attach to known cancer-causing proteins. He mentioned that this method may be used in any protein that is believed to be an alleged cause of diseases of any type, not just cancer.