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PROVIDENCE, R.I. [Brown University] As the 2022-23 academic year begins at Brown and more than 3,203 new undergraduate, graduate and medical students arrive on College Hill, the University also welcomes a group of 62 dynamic new scholars and educators to the Brown faculty.

With research and teaching expertise on topics ranging from global health security to climate change, brain science to diaspora studies, and architecture to algorithms, Browns newest faculty members represent a wide range of fields, backgrounds and viewpoints. Welcoming faculty with such diversity of experience comes at a time when Brown is developing an operational plan for significantly growing its research enterprise.

Our faculty are accomplished researchers, creative artists and educators whose work expands knowledge at the leading edge of their disciplines and, in partnership with Brown students and staff, makes a positive impact in communities both locally and across the globe, said Provost Richard M. Locke. Im delighted to welcome this cohort of talented scholars to Brown to build on and amplify efforts across campus to develop solutions to many of the most vexing questions faced by society today.

The arrival of 62 new faculty members reflects Brown's strategic efforts to recruit top scholars from research areas articulated in the Building on Distinction strategic plan and related operational plans in which the University is uniquely poised to have a meaningful impact. And with nearly 30% of this years new faculty members coming from groups historically underrepresented in higher education, strategic investments in actions outlined in Brown's Diversity and Inclusion Action Plan are helping to ensure that faculty represent the varied backgrounds, perspectives and experiences critical to advancing knowledge, learning and discovery.

In addition, multiple new deans (including those with interim appointments) will lead schools and academic units in their first full academic years in 2022-23: Ronald Aubert, interim dean of the School of Public Health; Tejal Desai, dean of the School of Engineering; Dr. Mukesh K. Jain, dean of the Warren Alpert Medical School; Thomas Lewis, interim dean of the Graduate School; Shankar Prasad, dean of the School of Professional Studies; and Leah VanWey, dean of the faculty.

Desai, who began her tenure at Brown on Sept. 1, will deliver the keynote address at this years Opening Convocation ceremony on Saturday, Sept. 10.

The new professors, associate professors, assistant professors and lecturers will serve across the Universitys academic departments, divisions and schools. The full listing of new faculty, with links for more information about each of them, follows below.

Jayna BrownProfessor of Theatre Arts and Performance Studies

John EasonWatson Family University Associate Professor of Sociology and International and Public Affairs

Kent KleinmanFaculty Director of the Brown Arts Institute, Professor of the Practice of History of Art and Architecture

Karin WulfBeatrice and Julio Mario Santo Domingo Director and Librarian of the John Carter Brown Library, Professor of History

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Brown welcomes talented group of 62 new faculty members - Brown University

The University of Helsinki (https://www.helsinki.fi/en) is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world. We are an equal opportunity employer and offer an attractive and diverse workplace in an inspiring environment with a variety of development opportunities and benefits.

As a part of the Faculty of Science, the Department of Computer Science (https://www.helsinki.fi/en/computer-science) is a leading unit in Finland in its area and responsible for the teaching and research in computer science at the University of Helsinki. The number of professors at the department has grown in recent years and there are now 32 professorships. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems.The department has extensive international collaboration with companies and universities. Within teaching, the departments professors and staff are in charge of the Bachelors, Masters, and Doctoral Programmes in Computer Science, as well as the separate Masters Programme in Data Science, in which other departments also participate.

The Department of Computer Science at the Faculty of Science of the University of Helsinki invites applications for a

SWEDISH-SPEAKING PROFESSOR OR ASSISTANT/ASSOCIATE PROFESSOR IN COMPUTER SCIENCE

aiming to strengthen research areas in computer science at the university. We are looking for a new professor or assistant/associate professor to carry out research into the field of computer science in the departments current focus areas. Specifically, we invite candidates in artificial intelligence (especially societal aspects of AI), networking (especially cyber security), software engineering, or algorithms to apply, but highly qualified applicants in other areas of computer science will also be considered.

The person to be chosen must have a strong scientific track record in their specific field of computer science evidenced by publications in top tier forums of the field. The selected candidate must possess excellent skills in Swedish language as the teaching duties of this position are to be carried out in Swedish.

For more information on the position, qualifications and applying, please check the Swedish version of the job announcement.

The deadline for applications is October 25, 2022.

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Swedish-Speaking Professor or Assistant / Associate Professor in Computer Science job with UNIVERSITY OF HELSINKI | 307838 - Times Higher Education

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 MITs 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 youre 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 were going to utilize AlphaFold effectively and extensively in drug discovery, Collins says.

Better predictions

One possible reason for the models 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. Were 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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Analyzing the potential of AlphaFold in drug discovery - MIT News