Drug discovery

Visiting Scientist Alice Stanton speaks with Scientific American reporter Tanya Lewis about her work developing miBrain, a 3D model of the human aimed at helping scientists “better understand neurological diseases such as Alzheimer’s and Parkinson’s so researchers can develop personalized treatments for them.” Says Stanton of the need for stable support for scientific research: “When we have a loved one who gets sick, we want a treatment—we want something to cure them. It doesn’t come out of thin air.”

Nature reporter Jyoti Madhusoodanan features Prof. Regina Barzilay and Prof. James Collin’s work developing AI tools aimed at accelerating the process of drug discovery and tackling the growing problem of antibiotic resistance. Barzilay notes that the goal of AI-based drug design is not to have the perfect method, but to find working solutions to the antibiotic-resistance crisis. “To me, the art is really in taking the tools we currently have, which are already doing quite a bit, and translating them into something which is useful in clinic,” she explains. 

Writing for The Boston Globe, President Sally Kornbluth emphasizes the importance of investment in discovery science, what she calls “curiosity on a mission." Kornbluth notes: “When someone we love needs therapies that could have emerged but didn’t or when other countries now investing in science can launch new science-based industries or run their societies on vast resources of fusion energy or reap the benefits of quantum computing power or advanced medical breakthroughs, America will wish it sustained its leadership in scientific research here and now.”

Prof. James Collins and his colleagues are using AI to develop new compounds to combat the growing problem of antibiotic resistant bacteria, reports Lisa M. Krieger for the San Francisco Chronicle. Thus far, “Collins and his colleagues have synthesized several compounds that combat hard-to-treat infections of gonorrhea and MRSA,” writes Krieger. “These techniques are also being harnessed to fight diseases, like cancer, lupus and arthritis.” 

Prof. James Collins speaks with Financial Times reporter Patrick Temple-West about his work using AI to design new antibiotic compounds to combat drug-resistant bacteria. “At present, the [AI] models are doing quite well at designing compounds that can attack in a Petri dish,” says Collins. 

In an effort to better understand how protein language models (PLMs) think and better judge their reliability, MIT researchers applied a tool called sparse autoencoders, which can be used to make large language models more interpretable. The findings “may help scientists better understand how PLMs come to certain conclusions and increase researchers’ trust in them," writes Andrea Luis for The Scientist. 

Prof. Pierre Azoulay speaks with WBUR’s Martha Bebinger about a new study examining the potential impact of NIH budget cuts on the development of new medicines. Azoulay and his colleagues found that “more than half of drugs approved by the FDA since 2000 used NIH-funded research that would likely not have happened if the NIH had operated with a 40% smaller budget,” Bebinger explains. 

Fierce Biotech reporter Darren Incorvaia writes that a new study by MIT researchers demonstrates how potential NIH budget cuts could endanger the development of new medications. The researchers found that if the NIH budget had been 40% smaller from 1980 to 2007, the level of NIH cuts currently being proposed, “the science underlying numerous drugs approved in the 21st century would not have been funded,” Incorvaia explains. The findings suggest that “massive cuts of the kind that are being contemplated right now could endanger the intellectual foundations of the drugs of tomorrow,” explains Professor Pierre Azoulay. 

A new study co-authored by MIT researchers finds that more than half of the drugs approved by the FDA since 2000 are connected to NIH research that would be impacted by proposed 40 percent budget cuts, reports Genetic Engineering & Biotechnology News. 

MIT researchers have developed Boltz-2, an AI algorithm “that unites protein folding and prediction of small-molecule binding affinity in one package,” reports Laura Howes for Chemical & Engineering News. “The researchers say their new AI model approaches the level of accuracy achieved by traditional computational chemistry—such as methods involving free-energy perturbation calculations—but much more quickly and cheaply,” explains Howes. 

Researchers at MIT and elsewhere have developed Boltz-2, an open-source generative AI model that can help researchers find new medicines faster, reports Alex Knapp for Forbes. The tool “can not only predict the structure of proteins, it can also predict its binding affinity–that is, how well a potential drug is able to interact with that protein,” explains Knapp. “This is crucial in the early stages of developing a new medicine.” 

Wall Street Journal reporter Dominique Mosbergen spotlights how Prof. James Collins and his lab have built their “own algorithms to trawl chemical databases, such as those of existing pharmaceutical drugs, for potential antibacterial compounds.” Collins’ His lab is “also experimenting with using generative AI to design completely new molecules that could kill bacteria,” writes Mosbergen. 

Former postdoctoral associate Wen Shuhao and postdoctoral fellows Ma Jian and Lai Lipeng co-founded Xtalpi, a biotech startup that “uses AI and quantum physics-based calculations to find suitable structures that are fit for drug making,” reports Zinnia Lee for Forbes. The company plans to expand their technology to other industries such as solar panels and electric vehicle batteries. 

Cofounded by postdoctoral associate Wen Shuhao and postdoctoral fellows Ma Jian and Lai Lipeng, biotech startup Xtalpi "combines AI, quantum physics, cloud computing and robotic automation to find novel molecules that could be developed into new medicines,” reports Zinnia Lee for Forbes. “Xtalpi has also recently expanded into discovering new chemical compounds for applications such as agriculture, cosmetics, healthcare, as well as petrochemicals and new materials for electric vehicle batteries,” writes Lee.

Prof. Regina Barzilay joins The Economist’s “Babbage” podcast to discuss how artificial intelligence could enable health care providers to understand and treat diseases in new ways. Host Alok Jha notes that Barzilay is determined to “overcome those challenges that are standing in the way of getting AI models to become useful in health care.” Barzilay explains: “I think we really need to change our mindset and think how we can solve the many problems for which human experts were unable to find a way forward.”