There are many ways to kill microbes that cause dangerous infections. Combining genetic screening with machine learning can help researchers identify new antimicrobials.
Sampling marine sponges for bioprospecting.
Davide Seveso
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.
Artificial intelligence can be used to develop new drugs, quickly and cheaply.
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Bacterial infections are a growing global challenge. This is due to antibiotic-resistant bacteria, and researchers are turning to AI to develop new drugs.
Constraining drugs to a single function in the body may be limiting their full potential.
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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.
Around 75% of antibiotics, including penicillin and amphotericin B, are derived from natural products.
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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.
Depending on how you look at it, drugs that can act on multiple targets could be a boon instead of a challenge.
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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.
A factor holding back African research is the lack of strong collaborative networks between African laboratories and institutions.
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Drug discovery research in Africa receives modest but essential international funding through philanthropic foundations and selected pharmaceutical companies.
Pan-assay interference compounds, or PAINS, often come up as false positives when researchers screen for potential drug candidates.
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The path to using old drugs for COVID is full of potholes. So why are we using the same old flawed methods when we actually know what works?
The subtleties of how genes are transcribed into RNA molecules like the one depicted here are key to understanding the inner workings of cells.
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Shang Gao, University of Illinois Chicago and 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.
The pipes imprinted on microfluidic chips are about the size of a human hair, and in many ways are like miniaturizing a chemical manufacturing plant.
(Katherine Elvira)
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.
Gene-based vaccines had never been approved for humans before the coronavirus pandemic.
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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.
It takes a tremendous amount of computing power to simulate all the components and behaviors of viruses and cells.
Copyright: Thomas Splettstoesser scistyle.com
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