Regina Barzilay

About

Regina Barzilay is a Distinguished Professor for AI and Health at MIT CSAIL, specializing in artificial intelligence and decision-making with a focus on healthcare and life sciences. Her research areas include artificial intelligence and machine learning, natural language and speech processing, and AI applications in healthcare. She leverages computational, theoretical, and experimental tools to develop groundbreaking sensors, energy transducers, and physical substrates for computation, addressing shared challenges facing humanity. Her work combines intellectual traditions from computer science and electrical engineering to develop techniques for systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. Her contributions are recognized within the MIT community and the broader field of electrical engineering and computer science.

Research topics

• Computer Science
• Artificial Intelligence
• Chemistry
• Materials science
• Nanotechnology
• Microbiology
• Biochemistry
• Biology
• Cognitive science
• Computational biology

Selected publications

• A Deep Learning Approach to Antibiotic Discovery

  Cell · 2020 · 2127 citations

  DOI

• De novo design of protein structure and function with RFdiffusion

  Nature · 2023 · 1810 citations

  have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, prot…

  DOI

• Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

  Accounts of Chemical Research · 2020 · 369 citations

  Senior authorCorresponding

  Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the s…

  DOI

• Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

  Journal of Medicinal Chemistry · 2020 · 241 citations

  synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS…

  DOI

• Protein codes promote selective subcellular compartmentalization

  Science · 2025-02-06 · 76 citations

  articleOpen accessCorresponding

  Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guid…

  PublisherOA PDFDOI

Recent grants

• CAREER: Content and Cohesion Models, with Applications to Text Summarization and Natural Language Generation

  NSF · $400k · 2005–2012

• Automatic Processing of Spoken and Written Lecture Material

  NSF · $825k · 2004–2008

• SGER: Reconstructing the Tower of Babel: Cross-lingual Language Learning

  NSF · $56k · 2008–2009

Frequent coauthors

• Tommi Jaakkola

  174 shared

• Adam Yala

  Maternity and Children's Hospital

  67 shared

• Tal Schuster

  57 shared

• Constance D. Lehman

  Massachusetts General Hospital

  55 shared

• Wengong Jin

  54 shared

• Karthik Narasimhan

  42 shared

• Connor W. Coley

  Massachusetts Institute of Technology

  38 shared

• Kyle Swanson

  Purdue University West Lafayette

  25 shared

Education

• Ph.D., Computer Science

  Massachusetts Institute of Technology

  1993

• M.S., Computer Science

  Massachusetts Institute of Technology

  1989

• B.S., Computer Science

  Technion - Israel Institute of Technology

  1985

Awards & honors

• 2025 IEEE honors

Similar researchers at Massachusetts Institute of Technology

• Hari Balakrishnanh-104
• Elfar Adalsteinssonh-76
• Marc Baldoh-75