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Math approach may make drug discovery more effective, efficient

Math approach may make drug discovery more effective, efficient

If you think about it, the approach most people take to drug discovery is very inefficient. The traditional approach is to start with a lead compound, test it in vivo, observe the effects, and then tweak the structure of the compound in an attempt to optimize its therapeutic activity. However, this process is very hit-and-miss, and can often take years or even decades to yield a successful drug.

Now imagine if we could use mathematics to better understand the way drugs work, and to more efficiently design new ones. This is not a far-fetched scenario; in fact, it is already happening.

In recent years, mathematical models have been used to help design new drugs and to optimize existing ones. For example, a model was used to predict how well a certain drug would work against the influenza virus. This information was then used to design a more effective version of the drug.

Similarly, a model was used to design a new antimalarial drug that is much more effective than existing drugs. The new drug works by targeting a specific protein in the malaria-causing parasite, which the parasite needs for survival.

These are just two examples of how mathematical modelling can be used to improve drug design. The possibilities are endless, and as our understanding of biology improves, so too will our ability to design more effective and efficient drugs.

Despite its reputation as being difficult, math may actually be the key to more effective and efficient drug discovery, according to new research.

In a study published in the journal Nature Chemistry, a team of scientists from the University of Leicester in the UK and the Technical University of Munich in Germany used mathematical modelling to design and synthesize a new class of drug molecules.

The team used a technique called lattice mapping, which is a way of visualising data on a three-dimensional grid. By doing this, they were able to see the relationships between different molecules and predict how they would interact with each other.

This information allowed the scientists to design new molecules that were more likely to be effective against a target protein.

The team’s work could lead to a new way of designing drugs that is more efficient and cost-effective. It may also help to reduce the time it takes to get new drugs to market.

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