How Machines Learned to Discover Drugs

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

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Sally Kornbluth speaks at a podium to an audience of onlookers with Anne Klibanski seated beside her.

Explainable AI for Rational Antibiotic Discovery

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

A new computational technique could make it easier to engineer useful proteins

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

“Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It’s like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University. Learn more
Still of Regina Barzilay from AI Revolution

A.I. Revolution

Can we harness the power of artificial intelligence to solve the world’s most challenging problems without creating an uncontrollable force that ultimately destroys us? ChatGPT and other new A.I. tools can now answer complex questions, write essays, and generate realistic-looking images in a matter of seconds. They can even pass a lawyer’s bar exam. Should we celebrate? Or worry? Or both? Correspondent Miles O’Brien investigates how researchers are trying to transform the world using A.I., hunting for big solutions in fields from medicine to climate change. (Premiering March 27 at 9 pm on PBS) Learn more
Antibodies (pink) bind to influenza virus proteins (yellow) (artist’s conception)

‘A landmark moment’: scientists use AI to design antibodies from scratch

Researchers have used generative artificial intelligence (AI) to help them make completely new antibodies for the first time.

The proof-of-principle work, reported this week in a preprint on bioRxiv, raises the possibility of bringing AI-guided protein design to the therapeutic antibody market, which is worth hundreds of billions of dollars.

Antibodies — immune molecules that strongly attach to proteins implicated in disease — have conventionally been made using brute-force approaches that involve immunizing animals or screening vast numbers of molecules.

AI tools that can shortcut those costly efforts have the potential to “democratize the ability to design antibodies”, says study co-author Nathaniel Bennett, a computational biochemist at the University of Washington in Seattle. “Ten years from now, this is how we’re going to be designing antibodies.”

“It’s a really promising piece of research” that represents an important step in applying AI protein-design tools to making new antibodies, says Charlotte Deane, an immuno-informatician at the University of Oxford, UK. Learn more

Five MIT faculty members take on Cancer Grand Challenges

Cancer Grand Challenges recently announced five winning teams for 2024, which included five researchers from MIT: Michael Birnbaum, Regina Barzilay, Brandon DeKosky, Seychelle Vos, and Ömer Yilmaz. Each team is made up of interdisciplinary cancer researchers from across the globe and will be awarded $25 million over five years.

Birnbaum, an associate professor in the Department of Biological Engineering, leads Team MATCHMAKERS and is joined by co-investigators Barzilay, the School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and the AI faculty lead at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health; and DeKosky, Phillip and Susan Ragon Career Development Professor of Chemical Engineering. All three are also affiliates of the Koch Institute for Integrative Cancer Research At MIT. Learn more
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