There are many ways to kill microbes that cause dangerous infections. Combining genetic screening with machine learning can help researchers identify new antimicrobials.
Ancient microbes likely produced natural products their descendants today do not. Tapping into this lost chemical diversity could offer a potential source of new drugs.
Bacterial infections are a growing global challenge. This is due to antibiotic-resistant bacteria, and researchers are turning to AI to develop new drugs.
Despite technological advancements, many challenges remain in getting a drug from lab to pharmacy shelf. Reframing what is a “medicine” could expand treatment options for researchers and patients.
With the dual threats of antibiotic resistance and emerging pandemics, finding new drugs becomes even more urgent. A trove of medicines may be lying under our nose.
Gregory Way, University of Colorado Anschutz Medical Campus
Many approved drugs work on the body in ways that researchers still aren’t entirely clear about. Seeing this as an opportunity instead of a flaw may lead to better treatments for complex conditions.
Drug discovery research in Africa receives modest but essential international funding through philanthropic foundations and selected pharmaceutical companies.
Shang Gao, University of Illinois Chicago et Jalees Rehman, University of Illinois Chicago
Machine learning is great at finding patterns but doesn’t know what those patterns mean. Combine it with knowledge gained from genetic research and you have a powerful view into the workings of cells.
Artificial cells on tiny microfluidic chips can provide early insight into how new cancer drugs behave in cells, and why certain kinds of cancer are more resistant to chemotherapy treatment.
The coronavirus pandemic has driven a lot of scientific progress in the past year. But just as some of the social changes are likely here to stay, so are some medical innovations.
Scanning through billions of chemicals to find a few potential drugs for treating COVID-19 requires computers that harness together thousands of processors.
Professor and Director of Quantitative Biosciences Institute & Senior Investigator at the Gladstone Institutes, University of California, San Francisco