Using a neural network and machine learning, the research team will streamline the process of identifying promising nanoparticles to serve as the next generation of antimicrobials. The work could curb dangerous drug-resistant bacteria, lower drug R&D costs and shorten the time it takes to get new effective medications on the market. Today it typically takes 14 years to identify a promising compound, turn it into a drug and make it available to patients. And only 14 percent of the drugs that begin testing end up in the market. With their new approach, the researchers believe they can raise that percentage of useful compounds to 80 over time. This next generation drug development method will save lives and put both the College of Engineering and U-M Medical School at the heart of industry innovation.

For the last 75 years, we’ve been relying on antibiotics to control bacteria. Unfortunately, drug-resistant bacteria continue to evolve and pose a threat to society, especially as the population continues to rise. In addition to the long time horizon for drug development, the high cost of antibiotic research and development pushed pharmaceutical companies into more lucrative options, such as cancer drugs.

With help from the Blue Sky Initiative, researchers will create an infrastructure for discovery, design, and development of the nano-antimicrobials society needs. Using machine learning, molecular simulations and biological experiments, the team will develop next-generation antimicrobial nanomaterials that use multiple avenues of attack to lower the odds of bacterial resistance.