Explores algorithms that leverage the geometric and spatial properties of molecular structures, incorporating symmetry, invariance, and equivariance principles to enhance model performance and interpretability in molecular modeling. Learn more
Today, the U.S. Department of Health and Human Services (HHS) through the Advanced Research Projects Agency for Health (ARPA-H) announced funding for the Transforming Antibiotic R&D with Generative AI to stop Emerging Threats (TARGET) project, which will use AI to speed the discovery and development of new classes of antibiotics. This program is another action to support the United States’ longstanding commitment to combating antimicrobial resistance (AMR), from groundbreaking innovation to international collaboration. The U.S. is a global leader in the fight against AMR and has a demonstrated track record of progress in protecting people, animals, and the environment from the threat of AMR domestically and globally.
“Antibiotic resistance is a real and urgent threat affecting millions of people. We need to prevent infections and conserve the antibiotics we have. We also urgently need new drugs to treat these increasingly resistant infections. This project will use AI to speed this needed innovation and help ensure we have the medicines we need to keep people alive,” said Secretary Xavier Becerra. Learn more
When I first became a doctor, I cared for an older man whom I’ll call Ted. He was so sick with pneumonia that he was struggling to breathe. His primary-care physician had prescribed one antibiotic after another, but his symptoms had only worsened; by the time I saw him in the hospital, he had a high fever and was coughing up blood. His lungs seemed to be infected with methicillin-resistant Staphylococcus aureus (MRSA), a bacterium so hardy that few drugs can kill it. I placed an oxygen tube in his nostrils, and one of my colleagues inserted an I.V. into his arm. We decided to give him vancomycin, a last line of defense against otherwise untreatable infections.
Ted recovered with astonishing speed. When I stopped by the next morning, he smiled and removed the oxygen tube, letting it dangle near his neck like a pendant. Then he pointed to the I.V. pole near his bed, where a clear liquid was dripping from a bag and into his veins.
“Where did that stuff come from?” Ted asked.
“The pharmacy,” I said.
“No, I mean, where did it come from?”
At the time, I could barely pronounce the names of medications, let alone hold forth on their provenance. “I’ll have to get back to you,” I told Ted. He was discharged before I could. But, in the years that followed, I often thought about his question. Every day, I administer medicines whose origins are a mystery to me. I occasionally meet a patient for whom I have no effective treatment to offer, and Ted’s inquiry starts to seem existential. Where do drugs come from, and how can we get more of them? Learn more
The use of AI to streamline drug discovery is exploding. Researchers are deploying machine-learning models to help them identify molecules, among billions of options, that might have the properties they are seeking to develop new medicines.
But there are so many variables to consider — from the price of materials to the risk of something going wrong — that even when scientists use AI, weighing the costs of synthesizing the best candidates is no easy task.
The myriad challenges involved in identifying the best and most cost-efficient molecules to test is one reason new medicines take so long to develop, as well as a key driver of high prescription drug prices.
To help scientists make cost-aware choices, MIT researchers developed an algorithmic framework to automatically identify optimal molecular candidates, which minimizes synthetic cost while maximizing the likelihood candidates have desired properties. The algorithm also identifies the materials and experimental steps needed to synthesize these molecules. Learn more
Researchers now employ artificial intelligence (AI) models based on deep learning to make functional predictions about big datasets. While the concepts behind these networks are well established, their inner workings are often invisible to the user. The emerging area of explainable AI (xAI) provides model interpretation techniques that empower life science researchers to uncover the underlying basis on which AI models make such predictions.
In this month’s episode, Deanna MacNeil from The Scientist spoke with Jim Collins from the Massachusetts Institute of Technology to learn how researchers are using explainable AI and artificial neural networks to gain mechanistic insights for large scale antibiotic discovery. Learn more
Since the 1970s, modern antibiotic discovery has been experiencing a lull. Now the World Health Organization has declared the antimicrobial resistance crisis as one of the top 10 global public health threats.
When an infection is treated repeatedly, clinicians run the risk of bacteria becoming resistant to the antibiotics. But why would an infection return after proper antibiotic treatment? One well-documented possibility is that the bacteria are becoming metabolically inert, escaping detection of traditional antibiotics that only respond to metabolic activity. When the danger has passed, the bacteria return to life and the infection reappears.
“Resistance is happening more over time, and recurring infections are due to this dormancy,” says Jackie Valeri, a former MIT-Takeda Fellow (centered within the MIT Abdul Latif Jameel Clinic for Machine Learning in Health) who recently earned her PhD in biological engineering from the Collins Lab. Valeri is the first author of a new paper published in this month’s print issue of Cell Chemical Biology that demonstrates how machine learning could help screen compounds that are lethal to dormant bacteria. Learn more
