Analyzing the potential of AlphaFold in drug discovery

Over the past few decades, very few new antibiotics have been developed, largely because current methods for screening potential drugs are prohibitively expensive and time-consuming. One promising new strategy is to use computational models, which offer a potentially faster and cheaper way to identify new drugs.

A new study from MIT reveals the potential and limitations of one such computational approach. Using protein structures generated by an artificial intelligence program called AlphaFold, the researchers explored whether existing models could accurately predict the interactions between bacterial proteins and antibacterial compounds. If so, then researchers could begin to use this type of modeling to do large-scale screens for new compounds that target previously untargeted proteins. This would enable the development of antibiotics with unprecedented mechanisms of action, a task essential to addressing the antibiotic resistance crisis.

However, the researchers, led by James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, found that these existing models did not perform well for this purpose. In fact, their predictions performed little better than chance.

“Breakthroughs such as AlphaFold are expanding the possibilities for in silico drug discovery efforts, but these developments need to be coupled with additional advances in other aspects of modeling that are part of drug discovery efforts,” Collins says. “Our study speaks to both the current abilities and the current limitations of computational platforms for drug discovery.”

In their new study, the researchers were able to improve the performance of these types of models, known as molecular docking simulations, by applying machine-learning techniques to refine the results. However, more improvement will be necessary to fully take advantage of the protein structures provided by AlphaFold, the researchers say.

Collins is the senior author of the study, which appears today in the journal Molecular Systems Biology. MIT postdocs Felix Wong and Aarti Krishnan are the lead authors of the paper.

Molecular interactions

The new study is part of an effort recently launched by Collins’ lab called the Antibiotics-AI Project, which has the goal of using artificial intelligence to discover and design new antibiotics.

AlphaFold, an AI software developed by DeepMind and Google, has accurately predicted protein structures from their amino acid sequences. This technology has generated excitement among researchers looking for new antibiotics, who hope that they could use the AlphaFold structures to find drugs that bind to specific bacterial proteins.

To test the feasibility of this strategy, Collins and his students decided to study the interactions of 296 essential proteins from E. coli with 218 antibacterial compounds, including antibiotics such as tetracyclines.

The researchers analyzed how these compounds interact with E. coli proteins using molecular docking simulations, which predict how strongly two molecules will bind together based on their shapes and physical properties.

This kind of simulation has been successfully used in studies that screen large numbers of compounds against a single protein target, to identify compounds that bind the best. But in this case, where the researchers were trying to screen many compounds against many potential targets, the predictions turned out to be much less accurate.

By comparing the predictions produced by the model with actual interactions for 12 essential proteins, obtained from lab experiments, the researchers found that the model had false positive rates similar to true positive rates. That suggests that the model was unable to consistently identify true interactions between existing drugs and their targets.

Using a measurement often used to evaluate computational models, known as auROC, the researchers also found poor performance. “Utilizing these standard molecular docking simulations, we obtained an auROC value of roughly 0.5, which basically says you’re doing no better than if you were randomly guessing,” Collins says.

The researchers found similar results when they used this modeling approach with protein structures that have been experimentally determined, instead of the structures predicted by AlphaFold.

“AlphaFold appears to do roughly as well as experimentally determined structures, but we need to do a better job with molecular docking models if we’re going to utilize AlphaFold effectively and extensively in drug discovery,” Collins says.

Better predictions

One possible reason for the model’s poor performance is that the protein structures fed into the model are static, while in biological systems, proteins are flexible and often shift their configurations.

To try to improve the success rate of their modeling approach, the researchers ran the predictions through four additional machine-learning models. These models are trained on data that describe how proteins and other molecules interact with each other, allowing them to incorporate more information into the predictions.

“The machine-learning models learn not just the shapes, but also chemical and physical properties of the known interactions, and then use that information to reassess the docking predictions,” Wong says. “We found that if you were to filter the interactions using those additional models, you can get a higher ratio of true positives to false positives.”

However, additional improvement is still needed before this type of modeling could be used to successfully identify new drugs, the researchers say. One way to do this would be to train the models on more data, including the biophysical and biochemical properties of proteins and their different conformations, and how those features influence their binding with potential drug compounds.

This study both lets us understand just how far we are from realizing full machine-learning-based paradigms for drug development, and provides fantastic experimental and computational benchmarks to stimulate and direct and guide progress towards this future vision,” says Roy Kishony, a professor of biology and computer science at Technion (the Israel Institute of Technology), who was not involved in the study.

With further advances, scientists may be able to harness the power of AI-generated protein structures to discover not only new antibiotics but also drugs to treat a variety of diseases, including cancer, Collins says. “We’re optimistic that with improvements to the modeling approaches and expansion of computing power, these techniques will become increasingly important in drug discovery,” he says. “However, we have a long way to go to achieve the full potential of in silico drug discovery.”

The research was funded by the James S. McDonnell Foundation, the Swiss National Science Foundation, the National Institute of Allergy and Infectious Diseases, the National Institutes of Health, and the Broad Institute of MIT and Harvard. The Antibiotics-AI Project is supported by the Audacious Project, the Flu Lab, the Sea Grape Foundation, and the Wyss Foundation.

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Forging political alliances through supply chains

The bustling coastal city of Wenzhou made its mark early on Hao Zhang. “It is considered the birthplace of Chinese capitalism, and living there, with many relatives involved in startups, I was exposed to all kinds of business,” says Zhang, a rising fifth-year graduate student in political science. He also gained a close-up view of interactions between government and private enterprise. “My father dealt with these firms all his life as a tax collector,” he says. “I learned from him how important the relationship is between business and government.”

Zhang grew fascinated with the expansion of private enterprise into international markets, and the emergence of government institutions in response to this explosive economic growth. “I became really interested in the broader narrative of China’s rise, with the increased tensions between China and the U.S. and the western world in general,” he says.

Today, Zhang is engaged in a unique investigation of the vast global networks sustaining China’s expanding economic and political reach. His research, built on mountainous datasets, reveals that firms with supply chain connections, including those that cross borders, also share political goals. Identifying these complex and often obscure interdependencies is essential to “advancing our understanding of trade politics,” Zhang states. At a time when pandemic, war, and climate change are buffeting trade networks, Zhang believes his supply chain research has “important implications for the U.S. and the world.”

Production linkages

Zhang came to MIT in 2018. He had previously conducted research on the impacts of Chinese government involvement in foreign investment and its use of tax-break policies on business development, and intended to continue in the same vein.

But the Trump Administration’s trade war with China helped shift his focus. When the U.S. government placed punitive tariffs on Chinese industries sending goods to the U.S — including textiles and computer chips — Zhang noted an interesting phenomenon: firms involved in manufacturing and distributing these products banded together to object. “Chinese companies and those in the U.S. and elsewhere connected to Chinese firms through supply chains found their interests aligned, and felt it was important to stand up and lobby for themselves and their foreign partners,” he says.

Zhang realized that these alliances, formed across great distances, represented a previously unremarked but nevertheless critical aspect of trade politics. “The political strategies involved in achieving trade goals are fundamental questions in political economy,” says Zhang. Traditionally, scholars study the effects on trade of labor and capital interests, or the needs of industries, or the protectionist inclinations of firms with either domestic or international markets. “But what have been missing,” he says, “are the political impacts of production linkages between firms and industries through global supply chains.”

Amassing the evidence

To analyze the degree of political coordination among firms with shared supply chains, Zhang connected two databases: one included all publicly listed U.S. firms and members of their supply chains from 2003 to 2020; and the other showed these firms’ lobbying reports, from 2004 to 2019. Each database contained millions of observations. (The datasets emerged from the LobbyView and TradeLab projects, led by In Song Kim, associate professor of political science and one of Zhang’s thesis advisors.)

Quantifying the production and lobbying linkages proved a big challenge, says Zhang, requiring cutting-edge data science techniques and two years of work. “I’m maybe the first political scientist to pose questions about the linkages, and analyze these datasets together,” he remarks.

The fruits of Zhang’s labor, which form the core of his book-length thesis project, include some intriguing insights: U.S. firms linked to the same foreign partner (for example, to the Chinese tech giant Huawei, before it was prohibited from selling in U.S. markets) are more likely to lobby together. “Looking at the databases, I could see how their lobbying expenses were correlated,” says Zhang. And when trade barriers hit the Chinese textile and apparel industry, firms involved with these goods pushed U.S. trade associations to lobby on their behalf. “When the U.S. and China started a trade war, lobbying expenses spiked in a coordinated way,” he says.

The lesson Zhang draws is that traditional policy tools like tariffs can’t be used to protect a nation’s products. “It’s impossible, because whether it’s made by Apple or another firm, a product has lots of different components from many different countries, and imposing a tariff brings collateral damage to firms, industries, and workers at home,” he says. “Interests are blurred now, because industries are interconnected by production relationships.”

Toward better trade policy

It was a swift path for Zhang to scholarship in the field of international political economy. He headed to Renmin University in Beijing for undergraduate study in political science. Then in his sophomore year, he attended the University of California at Berkeley on a scholarship. The experience changed his life.

“I learned how to study in the authentic American style — how to formulate interesting questions and gather data,” he says. “That was the moment when I decided to pursue a higher degree.” Zhang first earned master’s degrees from Tsinghua University in international politics, and Johns Hopkins SAIS in international economics and China studies. He then set his cap on a political science graduate program in the U.S., and specifically at MIT, with its reputation for solving problems using quantitative methods.

Zhang leapt into a new life as a Chinese student in Cambridge, Massachusetts. He joined MIT’s International Student Office as a graduate fellow, hoping “to help international students settle down in school and thrive in this environment,” he says. In recent years, Chinese students have faced “difficult situations, with Covid and the last president making them feel alone and unwelcome,” he says.

After nearly eight years in the U.S., Zhang feels “part Americanized,” but he regards himself as Chinese, and has continued to read Chinese classical poetry and philosophy, as well as practice Chinese calligraphy — an art that he learned at age 7. “As a researcher, I’m constantly under high pressure to produce, and calligraphy is spiritually beneficial to me,” he says.

As he continues analyzing his doctoral data, Zhang is convinced that conventional, protectionist trade tools no longer suit an increasingly enmeshed and embattled global economy. He is studying policy alternatives that might serve better to balance between firms favoring protectionist measures and those advocating free trade. He feels sure that his current work will be of practical interest to governments and businesses seeking information on the supply chain relations of leading firms. A kink in one part of a sprawling network inevitably affects the rest, and identifying all these partners in advance could help make these chains more resilient. “I think my work could eventually help alleviate global production problems,” he says.

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Featured video: Thank you, MIT employees!

Every fall, some 7,000 students arrive on campus, joining around 5,000 already here. In preparation, staff and faculty have made ready 6,210 dorm rooms, 30 residence halls, 13,828 offices, 5,488 labs, and 119 academic buildings.

In honor of Labor Day, in this video we say, 'Thank you, MIT employees!'

Video by Melanie Gonick/MIT 

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Using machine learning to identify undiagnosable cancers

The first step in choosing the appropriate treatment for a cancer patient is to identify their specific type of cancer, including determining the primary site — the organ or part of the body where the cancer begins.

In rare cases, the origin of a cancer cannot be determined, even with extensive testing. Although these cancers of unknown primary tend to be aggressive, oncologists must treat them with non-targeted therapies, which frequently have harsh toxicities and result in low rates of survival.

A new deep-learning approach developed by researchers at the Koch Institute for Integrative Cancer Research at MIT and Massachusetts General Hospital (MGH) may help classify cancers of unknown primary by taking a closer look the gene expression programs related to early cell development and differentiation.

“Sometimes you can apply all the tools that pathologists have to offer, and you are still left without an answer,” says Salil Garg, a Charles W. (1955) and Jennifer C. Johnson Clinical Investigator at the Koch Institute and a pathologist at MGH. “Machine learning tools like this one could empower oncologists to choose more effective treatments and give more guidance to their patients.”

Garg is the senior author of a new study, published Aug. 30 in Cancer Discovery. The artificial intelligence tool is capable of identifying cancer types with a high degree of sensitivity and accuracy. Garg is the senior author of the study, and MIT postdoc Enrico Moiso is the lead author.

Machine learning in development

Parsing the differences in the gene expression among different kinds of tumors of unknown primary is an ideal problem for machine learning to solve. Cancer cells look and behave quite differently from normal cells, in part because of extensive alterations to how their genes are expressed. Thanks to advances in single cell profiling and efforts to catalog different cell expression patterns in cell atlases, there are copious — if, to human eyes, overwhelming — data that contain clues to how and from where different cancers originated.

However, building a machine learning model that leverages differences between healthy and normal cells, and among different kinds of cancer, into a diagnostic tool is a balancing act. If a model is too complex and accounts for too many features of cancer gene expression, the model may appear to learn the training data perfectly, but falter when it encounters new data. However, by simplifying the model by narrowing the number of features, the model may miss the kinds of information that would lead to accurate classifications of cancer types.

In order to strike a balance between reducing the number of features while still extracting the most relevant information, the team focused the model on signs of altered developmental pathways in cancer cells. As an embryo develops and undifferentiated cells specialize into various organs, a multitude of pathways directs how cells divide, grow, change shape, and migrate. As the tumor develops, cancer cells lose many of the specialized traits of a mature cell. At the same time, they begin to resemble embryonic cells in some ways, as they gain the ability to proliferate, transform, and metastasize to new tissues. Many of the gene expression programs that drive embryogenesis are known to be reactivated or dysregulated in cancer cells.

The researchers compared two large cell atlases, identifying correlations between tumor and embryonic cells: the Cancer Genome Atlas (TCGA), which contains gene expression data for 33 tumor types, and the Mouse Organogenesis Cell Atlas (MOCA), which profiles 56 separate trajectories of embryonic cells as they develop and differentiate.

“Single-cell resolution tools have dramatically changed how we study the biology of cancer, but how we make this revolution impactful for patients is another question,” explains Moiso. “With the emergence of developmental cell atlases, especially ones that focus on early phases of organogenesis such as MOCA, we can expand our tools beyond histological and genomic information and open doors to new ways of profiling and identifying tumors and developing new treatments.”

The resulting map of correlations between developmental gene expression patterns in tumor and embryonic cells was then transformed into a machine learning model. The researchers broke down the gene expression of tumor samples from the TCGA into individual components that correspond to a specific point of time in a developmental trajectory, and assigned each of these components a mathematical value. The researchers then built a machine-learning model, called the Developmental Multilayer Perceptron (D-MLP), that scores a tumor for its developmental components and then predicts its origin.

Classifying tumors

After training, the D-MLP was applied to 52 new samples of particularly challenging cancers of unknown primary that could not be diagnosed using available tools. These cases represented the most challenging seen at MGH over a four-year period beginning in 2017. Excitingly, the model classed the tumors to four categories, and yielded predictions and other information that could guide diagnosis and treatment of these patients.

For example, one sample came from a patient with a history of breast cancer who showed signs of an aggressive cancer in the fluid spaces around the abdomen. Oncologists initially could not find a tumor mass, and could not classify cancer cells using the tools they had at the time. However, the D-MLP strongly predicted ovarian cancer. Six months after the patient first presented, a mass was finally found in the ovary that proved to be the origin of tumor. 

Moreover, the study’s systematic comparisons between tumor and embryonic cells revealed promising, and sometimes surprising, insights into the gene expression profiles of specific tumor types. For instance, in early stages of embryonic development, a rudimentary gut tube forms, with the lungs and other nearby organs arising from the foregut, and much of the digestive tract forming from the mid- and hindgut. The study showed that lung-derived tumor cells showed strong similarities not just to the foregut as might be expected, but to the to mid- and hindgut-derived developmental trajectories. Findings like these suggest that differences in developmental programs could one day be exploited in the same way that genetic mutations are commonly used to design personalized or targeted cancer treatments.

While the study presents a powerful approach to classifying tumors, it has some limitations. In future work, researchers plan to increase the predictive power of their model by incorporating other types of data, notably information gleaned from radiology, microscopy, and other types of tumor imaging.

“Developmental gene expression represents only one small slice of all the factors that could be used to diagnose and treat cancers,” says Garg. "Integrating radiology, pathology, and gene expression information together is the true next step in personalized medicine for cancer patients.”

This study was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute and by the National Cancer Institute.

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High energy and hungry for the hardest problems

A high school track star and valedictorian, Anne White has always relished moving fast and clearing high hurdles. Since joining the Department of Nuclear Science and Engineering (NSE) in 2009 she has produced path-breaking fusion research, helped attract a more diverse cohort of students and scholars into the discipline, and, during a worldwide pandemic, assumed the role of department head as well as co-lead of an Institute-wide initiative to address climate change. For her exceptional leadership, innovation, and accomplishments in education and research, White was named the School of Engineering Distinguished Professor of Engineering in July 2020.

But White declares little interest in recognition or promotions. “I don’t care about all that stuff,” she says. She’s in the race for much bigger stakes. “I want to find ways to save the world with nuclear,” she says.

Tackling turbulence

It was this goal that drew White to MIT. Her research, honed during graduate studies at the University of California at Los Angeles, involved developing a detailed understanding of conditions inside fusion devices, and resolving issues critical to realizing the vision of fusion energy — a carbon-free, nearly limitless source of power generated by 150-million-degree plasma.

Harnessing this superheated, gaseous form of matter requires a special donut-shaped device called a tokamak, which contains the plasma within magnetic fields. When White entered fusion around the turn of the millennium, models of plasma behavior in tokamaks didn’t reliably match observed or experimental conditions. She was determined to change that picture, working with MIT’s state-of-the-art research tokamak, Alcator C-Mod.

White believed solving the fusion puzzle meant getting a handle on plasma turbulence — the process by which charged atomic particles, breaking out of magnetic confinement, transport heat from the core to the cool edges of the tokamak. Although researchers knew that fusion energy depends on containing and controlling the heat of plasma reactions, White recalls that when she began grad school, “it was not widely accepted that turbulence was important, and that it was central to heat transport. She “felt it was critical to compare experimental measurements to first principles physics models, so we could demonstrate the significance of turbulence and give tokamak models better predictive ability.”

In a series of groundbreaking studies, White’s team created the tools for measuring turbulence in different conditions, and developed computational models that could account for variations in turbulence, all validated by experiments. She was one of the first fusion scientists both to perform experiments and conduct simulations. “We lived in the domain between these two worlds,” she says.

White’s turbulence models opened up approaches for managing turbulence and maximizing tokamak performance, paving the way for net-energy fusion energy devices, including ITER, the world’s largest fusion experiment, and SPARC, a compact, high-magnetic-field tokamak, a collaboration between MIT’s Plasma Science and Fusion Center and Commonwealth Fusion Systems.

Laser-focused on turbulence

Growing up in the desert city of Yuma, Arizona, White spent her free time outdoors, hiking and camping. “I was always in the space of protecting the environment,” she says. The daughter of two lawyers who taught her “to argue quickly and efficiently,” she excelled in math and physics in high school. Awarded a full ride at the University of Arizona, she was intent on a path in science, one where she could tackle problems like global warming, as it was known then. Physics seemed like the natural concentration for her.

But there was unexpected pushback. The physics advisor believed her physics grades were lackluster. “I said, ‘Who cares what this guy thinks; I’ll take physics classes anyway,’” recalls White. Being tenacious and “thick skinned,” says White, turned out to be life-altering. “I took nuclear physics, which opened my eyes to fission, which then set me off on a path of understanding nuclear power and advanced nuclear systems,” she says. Math classes introduced her to chaotic systems, and she decided she wanted to study turbulence. Then, at a Society of Physics Students meeting White says she attended for the free food, she learned about fusion.

“I realized this was what I wanted to do,” says White. “I became totally laser focused on turbulence and tokamaks.”

At UCLA, she began to develop instruments and methods for measuring and modeling plasma turbulence, working on three different fusion research reactors, and earning fellowships from the Department of Energy (DOE) during her graduate and post-graduate years in fusion energy science. At MIT, she received a DOE Early Career Award that enabled her to build a research team that she now considers her “legacy.”

As she expanded her research portfolio, White was also intent on incorporating fusion into the NSE curriculum at the undergraduate and graduate level, and more broadly, on making NSE a destination for students concerned about climate change. In recognition of her efforts, she received the 2014 Junior Bose Teaching Award. She also helped design the EdX course, Nuclear Engineering: Science, Systems and Society, introducing thousands of online learners to the potential of the field. “I have to be in the classroom,” she says. “I have to be with students, interacting, and sharing knowledge and lines of inquiry with them.”

But even as she deepened her engagement with teaching and with her fusion research, which was helping spur development of new fusion energy technologies, White could not resist leaping into a consequential new undertaking: chairing the department. “It sounds cheesy, but I did it for my kid,” she says. “I can be helpful working on fusion, but I thought, what if I can help more by enabling other people across all areas of nuclear? This department gave me so much, I wanted to give back.”

Although the pandemic struck just months after she stepped into the role in 2019, White propelled the department toward a new strategic plan. “It captures all the urgency and passion of the faculty, and is attractive to new students, with more undergraduates enrolling and more graduate students applying,” she says. White sees the department advancing the broader goals of the field, “articulating why nuclear is fundamentally important across many dimensions for carbon-free electricity and generation.” This means getting students involved in advanced fission technologies such as nuclear batteries and small modular reactors, as well as giving them an education in fusion that will help catalyze a nascent energy industry.

Restless for a challenge

White feels she’s still growing into the leadership role. “I’m really enthusiastic and sometimes too intense for people, so I have to dial it back during challenging conversations,” she says. She recently completed a Harvard Business School course on leadership.

As the recently named co-chair of MIT’s Climate Nucleus (along with Professor Noelle Selin), charged with overseeing MIT’s campus initiatives around climate change, White says she draws on a repertoire of skills that come naturally to her: listening carefully, building consensus, and seeing value in the diversity of opinion. She is optimistic about mobilizing the Institute around goals to lower MIT’s carbon footprint, “using the entire campus as a research lab,” she says.

In the midst of this push, White continues to advance projects of concern to her, such as making nuclear physics education more accessible. She developed an in-class module involving a simple particle detector for measuring background radiation. “Any high school or university student could build this experiment in 10 minutes and see alpha particle clusters and muons,” she says.

White is also planning to host “Rising Stars,” an international conference intended to help underrepresented groups break barriers to entry in the field of nuclear science and engineering. “Grand intellectual challenges like saving the world appeal to all genders and backgrounds,” she says.

These projects, her departmental and institutional duties, and most recently a new job chairing DOE’s Fusion Energy Sciences Advisory Committee leave her precious little time for a life outside work. But she makes time for walks and backpacking with her husband and toddler son, and reading the latest books by female faculty colleagues, such as “The New Breed,” by Media Lab robotics researcher Kate Darling, and “When People Want Punishment,” by Lily Tsai, Ford Professor of Political Science. “There are so many things I don’t know and want to understand,” says White.

Yet even at leisure, White doesn’t slow down. “It’s restlessness: I love to learn, and anytime someone says a problem is hard, or impossible, I want to tackle it,” she says. There’s no time off, she believes, when the goal is “solving climate change and amplifying the work of other people trying to solve it.”

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Letter regarding proposed free expression statement from MIT

The following letter was sent to the MIT community today by President L. Rafael Reif and includes a letter sent to MIT faculty today by Provost Cynthia Barnhart, Chancellor Melissa Nobles, and Chair of the Faculty Lily L. Tsai.

To the members of the MIT community,

Last fall, I asked the provost, chancellor and chair of the faculty to assemble a special working group to take up the charge of exploring, on behalf of the community, a range of profound questions around freedom of expression and academic freedom – and today I share important progress.

Free expression in the life of MIT

As I wrote at the time, for an institute devoted to advancing knowledge and educating students, freedom of expression has always been, and must remain, a fundamental MIT value. We must ensure that different points of view – even views that some or all of us may reject or find offensive – are allowed to be heard and debated on our campus. I am convinced we must be prepared to endure such painful outcomes as the price of protecting free expression – the principle is that important.

Making room for the full range of thought and expression is not an end in itself. Rather, the right to free expression is a tool – a sharp tool – for enlarging understanding and uncovering truth.

As members of a community grounded in mutual respect, that fact demands responsibility from each of us. Our shared aim must be to create an environment where all of us can speak and all of us can listen, one that enables us to learn from one another through free, open and productive conversation. This will take practice. It will take compassion and care. And on those occasions when members of our community bear the cost of other people’s free expression, they deserve our respect, our understanding and our support in exercising their own right to free expression.

Though ideas like free expression and academic freedom may seem clear enough in theory, experience on our campus and across the country has shown that people of goodwill can have substantial disagreement about how to apply them in practice. We saw this last fall with the wide range of views around the Carlson lecture. The sometimes bitter national debate on these issues continues to underscore the practical value of establishing, for our own community, a clear shared understanding of and commitment to free expression and academic freedom.

The Ad Hoc Working Group on Free Expression

I am therefore very pleased to share the results of the Ad Hoc Working Group on Free Expression. With outstanding leadership from Institute Professor Penny Chisholm and Professor Phil Clay PhD '75, its members arrived at this proposed statement of principle on freedom of expression and academic freedom. They summarize their process and thinking in this thoughtful report.

I am greatly encouraged that the working group’s statement of principle reflects such a strong commitment to openness, and I am hopeful that the faculty can agree to endorse a statement in that spirit. Taking a broad, forward-looking approach, the working group’s report and its recommendations offer balanced guidance that will help the MIT community successfully navigate a wide range of scenarios in the future.

Next steps

This afternoon, Provost Cynthia Barnhart, Chancellor Melissa Nobles and Faculty Chair Lily Tsai asked the entire MIT faculty to consider the proposed statement of principle – and to join in a process of reflection and discussion. You can find the text of their letter below.

Ultimately, the principles and commitments that the statement puts forward must serve our whole community. We invite you to share your thoughts with the faculty officers as soon as possible at freexresponses@mit.edu.

With appreciation for everyone who has contributed to this important work,

L. Rafael Reif

-----

The following letter was sent to MIT faculty today by Provost Cynthia Barnhart, Chancellor Melissa Nobles, and Chair of the Faculty Lily L. Tsai.

Dear colleagues, Last fall, debate around the handling of the Carlson Lecture spurred our community to focus on important questions on freedom of expression and academic freedom. As part of that process, President Reif asked the provost, chancellor, and chair of the faculty to assemble a special working group to explore these questions. The Ad Hoc Working Group on Free Expression The members of the Ad Hoc Working Group on Free Expression pursued their charge through extensive research, outreach, reflection and debate. This included several forums last fall; substantial feedback received by the MIT Values Statement Committee; direct community input via email; and outreach to seek the views of students, staff, postdocs, alumni and MIT Corporation members. We are tremendously grateful to the working group – and to its co-chairs Institute Professor Penny Chisholm and Professor Phil Clay PhD '75 – for taking up this challenge with such seriousness and care. A proposed MIT Statement on Freedom of Expression and Academic Freedom Today, we share the statement of principle that the working group developed on freedom of expression and academic freedom – and we invite you to engage in deliberation and discussion of this statement. As the working group’s report explains, its members sought to create a statement of principle to be endorsed by the faculty and that would become a lasting touchstone for our community. To allow for focused discussion about the statement, we will host two online forums for faculty on Thursday, September 8th from 3-4:30 PM and on Thursday, September 22nd from 8:30-10 AM. The working group co-chairs expect to be present. Like the forums last fall, this session will consist mostly of faculty-to-faculty conversation in small, randomly assigned breakout groups. Online Faculty Forums Thursday, September 8th 3-4:30 PM Eastern Thursday, September 22nd 8:30-10 AM Eastern If you can’t attend either of the forums, please share your thoughts with the faculty officers as soon as possible at freexresponses@mit.edu. A separate letter this afternoon will invite the rest of the campus community to provide comments by email, too. As President Reif wrote in January, “Because faculty have direct and enduring responsibility for stewarding MIT’s core academic values, principles and practices, including academic freedom,” faculty members have taken the lead on this process thus far.  These responsibilities constitute the foundation of what we do, and we urge the whole faculty to attend to them at this important juncture. Freedom of expression in an inclusive community Earlier today, the entire campus community also received the MIT Strategic Action Plan for Belonging, Achievement, and Composition, a document that outlines and coordinates plans across the Institute on subjects often referred to with the shorthand expression, “DEI.” The strategic action plan and the report on free expression followed their own distinct paths to this moment.  We have a strong conviction that these efforts can thrive in concert with each other, and that both are essential to a thriving future for MIT. Sincerely, Cynthia Barnhart, Provost Melissa Nobles, Chancellor Lily L. Tsai, Chair of the Faculty

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MIT releases strategic action plan for belonging, achievement, and composition

MIT has released a new strategic action plan for belonging, achievement, and composition, intended to help the Institute forge a stronger sense of community and pursue excellence by tapping into talent globally.

The plan provides an Institute-wide framework for advancing diversity, equity, and inclusion across MIT, while allowing individual departments, labs, and offices to define and tailor their own efforts in this regard.

“Yet as in many academic communities across the country, even after decades of effort, there is essential work still ahead to make sure that everyone who earns a place here feels welcome,” MIT President L. Rafael Reif wrote in a letter to the MIT community about the plan. “The plan we share today draws on best practices from the past but in a way that is far more coordinated, while allowing opportunities for new thinking.”

Broadly, the plan’s aims are threefold: to enhance a sense of belonging at MIT, in the form of a connected community; to support achievement, by providing equitable opportunities; and to support a diverse campus population to help ensure MIT continues to be a magnet for the very best students, staff, faculty, and postdocs from across the world.

“This plan is really focused on the people who make up the MIT community,” says John Dozier, MIT’s Institute Community and Equity Officer (ICEO), who oversaw the creation of the strategic action plan. “We want everyone to feel like MIT is a place that is invested in helping them succeed. The plan is designed to help create the conditions for every member of the community to make their very best contributions to the Institute’s mission.”

Speaking the language of MIT

Today’s release of the strategic action plan follows a nearly two-year process of gathering ideas, reviewing past efforts, soliciting community feedback, and studying data. Starting in the fall of 2020, Dozier, Deputy ICEO Maryanne Kirkbride, and Associate Provost Tim Jamison convened the Strategic Action Plan Steering Team, a committee of three dozen faculty, staff, graduate and undergraduate students, and postdocs. They surfaced a list of 170 ideas for action — some based on previous reports to the Institute, others completely new — and released a draft of the plan in spring 2021. The steering team went on a listening tour to collect feedback from the campus, and held three dozen community engagement sessions on Zoom to collect direct feedback from people. 

“The different perspectives from students to staff to faculty were absolutely crucial in creating this plan,” says John Fernández, a professor of building technology in MIT’s Department of Architecture, head of house at Baker House, and a member of the steering team. “There was no stakeholder that was not essential.” At times, he adds, “There was disagreement that required continued conversation, that then led to some resolution. That is the mark of a good committee, that there were diverse perspectives that did get worked out, and entered into the plan.”

That outlook is shared by students who served on the steering team.

“The Strategic Action Plan represents a significant commitment by the Institute, and one which is long overdue,” says Bianca Lepe, a PhD candidate in the Department of Biological Engineering and a member of the steering team. “I’m excited because it means that MIT is committed to resourcing both money and people to these issues, as well as making a public commitment to hold itself accountable to the goals it sets out.”

“What’s different about this plan is that it really tries to bring together the whole campus community in bringing about change,” says steering team member Yu Jing Chen ’22, who just earned her undergraduate degree from MIT and is starting graduate studies in the Department of Urban Studies and Planning. “You see that from the structure that emphasizes working with the local [campus] units to create their own plans. I think it’s really important that it’s not just something the central administration is saying it will do by itself.”

From the onset of their deliberations, Dozier and the members of the steering team worked to include a diversity of opinions, perspectives, and voices from the MIT community. They wanted the plan to reflect the unique qualities of the Institute. 

“In our early discussions, we really tried to focus on how diversity, equity, and inclusion were understood and being put to work at MIT,” Dozier says. “What, exactly, did these words mean to people here? Was there anything different about them?” Those conversations, Dozier notes, led them on a path to reframing their work, and the plan, using slightly different terms.

Belonging, achievement, and composition are not synonyms for inclusion, equity, and diversity, Dozier notes. “But they resonated with us,” he says. “They made sense in a way that seemed more genuine, more like the language of MIT.”

How the plan is structured

The strategic action plan makes 13 specific commitments and lists more than 50 proposed actions the Institute will take across a wide range of domains. Some actions are intended to bolster existing Institute practices and operations; others create new resources for members of the community. The Institute’s senior leadership will work with the five schools and the MIT Schwarzman College of Computing, as well as major administrative and research units, to coordinate these initiatives.

“My hope is that the strategic action plan will transform the MIT community in a very positive way and make it a more inclusive place where some people who are not part of the dominant demographic feel more welcome, more involved, and more a part of the Institute,” says Asegun Henry SM ’06, PhD ’09, an associate professor and director of the ASE Research Group in the Department of Mechanical Engineering, and a member of the plan’s steering team. Even as a successful graduate student, Henry notes, he found MIT to be “extremely isolating.”

To help community members have a sense of belonging and support, the plan calls for, among other things, curricular innovations and funding for relevant academic research; the creation of a Wellbeing Lab; and improved coordination and communications about mental health resources.

To expand support in areas relating to achievement, the action plan calls for stronger undergraduate advising, a structured four-year alert system to signal when undergraduates may need academic help, more data about progression rates for graduate students, more professional development programs for postdocs and staff, and annual performance reviews for staff to acknowledge and support contributions to belonging, achievement, and composition. 

The plan also calls for academic programs to document their graduate admissions processes and selection criteria and collaborate on best practices; support for Institute-wide recruitment activities for excellent students from underrepresented backgrounds; better coordination and expanded partnerships with minority-serving institutions; and the development of best practices for staff, faculty, and postdoc searches. 

A “foundation” year to move forward 

The action plan begins with a foundation year, a startup period during which “local” campus units and organizations — such as departments, labs, and major administrative units — will define or refine their own efforts and align their specific circumstances to the Institute’s broad aims. 

“The foundation year is designed to help us create common ground and shared language,” says Kirkbride, who is also the director of MindHandHeart and has already been working closely with campus leaders on the plan’s implementation. “We want people to feel like they can accomplish these actions as part of how they do their work — not as an additional burden on top of what has been a very challenging couple of years.” To get there, “We need to work together, and we may need to rethink some of what we’re already doing,” Kirkbride adds.

“We really have two goals for the foundation year,” Dozier says. “First, we want to enable local conversations so people can consider their path forward. Second, we want every member of the community to feel like they are covered, like someone is helping plan for their success.”

Office hours with the ICEO

Dozier, Kirkbride, and Institute staff members are planning activities and programming throughout the academic year to support community members and leaders through the process.

Weekly drop-in office hours will start on Sept. 8. Dozier and Kirkbride have also been working closely with MIT’s new assistant deans for diversity, equity, and inclusion to help academic, administrative, and research units to engage with the action plan. This includes new tools, templates, trainings, and datasets to support planning at the local level.

“We can all appreciate that civil discourse, critical thinking, and empathy are at the core of how we create and sustain a sense of community at MIT,” Dozier says. “The plan is a complex and ambitious document, but these skills are really at its heart. This is how we create the environment that fuels our best work and supports the Institute’s mission.”

Past, present, and future

This is not the first time MIT has grappled with many of these issues, with some reports extending back a number of decades. For example, in 1999, the Institute released a report, “A Study on the Status of Women Faculty in Science at MIT,” following work by two successive committees, chaired by professor of biology Nancy Hopkins and professor of psychology Mary C. Potter; the report found serious inequities in the resources available to female faculty members.

In 2010, MIT issued its “Report on the Initiative for Faculty Race and Diversity,” following a committee assessment led by professor of chemical engineering Paula T. Hammond; that report found limited progress in many areas of MIT life concerning underrepresented faculty members.

Building on that important prior work, the current strategic action plan seeks to spur Institute-wide change in which MIT realizes long-held goals, with the central administration working in concert with individual units. In this sense, enhancing diversity, equity, and inclusion at MIT is not simply directed from above, or left to smaller, unsupported units. Rather it is a collaboration that can be driven from many directions.

“The goal is for the plan to be owned by everyone in the community,” says Chen. “There is a part for everybody to play in making MIT a more inclusive place, and there is a lot in which everybody can learn in this regard. This plan serves as a way for community members to not only learn about some of the challenges to equity MIT faces today, but also how we as an Institute plan to take action and how we as individuals come into it.”

Henry says: “It’s well-intended for sure. Based on the conversations that I’ve been a part of, I think there’s a genuine desire and intent for this to have a positive impact.”

Knowing the broad range of tasks and goals included in the strategic action plan, steering team members say they look forward to seeing MIT collectively work to weave the plan’s initiatives into its everyday practices.

“We’re taking in my opinion the crucial next steps in order to actualize it, rather than just talking about it,” Lepe says.

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