Publications

*Equal contribution. Co-corresponding author.

2023

Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution
Varun Shanker, Theodora Bruun, Brian Hie, and Peter Kim
bioRxiv (2023)
Generative artificial intelligence for de novo protein design
Adam Winnifrith, Carlos Outeiral, and Brian Hie
arXiv (2023)
Efficient evolution of human antibodies from general protein language models
Brian Hie, Varun Shanker, Duo Xu, Theodora Bruun, Payton Weidenbacher, Shaogeng Tang, Wesley Wu, John Pak, and Peter Kim
Nature Biotechnology (2023)
Image 2
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin*, Halil Akin*, Roshan Rao*, Brian Hie*, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, and Alexander Rives
Science (2023)
Machine learning for protein engineering
Kadina Johnston*, Clara Fannjiang*, Bruce Witmann*, Brian Hie*, Kevin Yang*, and Zachary Wu*
arXiv (2023)

2022

Image 2
A high-level programming language for generative protein design
Brian Hie*, Salvatore Candido*, Zeming Lin, Ori Kabeli, Roshan Rao, Nikita Smetanin, Tom Sercu, and Alexander Rives
bioRxiv (2022)
Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins
Brian Hie, Kevin Yang, and Peter Kim
Cell Systems (2022)
Predicting the mutational drivers of future SARS-CoV-2 variants of concern
M. Cyrus Maher, Istvan Bartha, Steven Weaver, Julia di Iulio, Elena Ferri, Leah Soriaga, Florian Lempp, Brian Hie, Bryan Bryson, Bonnie Berger, David Robertson, Gyorgy Snell, Herbert Virgin, Sergei Kosakovsky Pond, and Amalio Telenti
Science Translational Medicine (2022)
Adaptive machine learning for protein engineering
Brian Hie and Kevin Yang
Current Opinion in Structural Biology (2022)

2021

Algorithms for understanding and fighting infectious disease
Brian Hie
Massachusetts Institute of Technology, Doctoral Thesis (2021)
Schema: Metric learning enables interpretable synthesis of heterogeneous single-cell modalities
Rohit Singh*, Brian Hie*, Ashwin Narayan, and Bonnie Berger
Genome Biology (2021)
Learning the language of viral evolution and escape
Brian Hie, Ellen Zhong, Bonnie Berger, and Bryan Bryson
Science (2021)

2020

Leveraging uncertainty in machine learning accelerates biological discovery and design
Brian Hie, Bryan Bryson, and Bonnie Berger
Cell Systems (2020)
Computational methods for single-cell RNA sequencing
Brian Hie*, Josh Peters*, Sarah Nyquist*, Alex Shalek, Bonnie Berger, and Bryan Bryson
Annual Review of Biomedical Data Science (2020)

2019

Geometric sketching compactly summarizes the single-cell transcriptomic landscape
Brian Hie*, Hyunghoon Cho*, Benjamin DeMeo, Bryan Bryson, and Bonnie Berger
Cell Systems (2019)
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Brian Hie, Bryan Bryson, and Bonnie Berger
Nature Biotechnology (2019)
Fine-mapping cis-regulatory variants in diverse human populations
Ashley Tehranchi, Brian Hie, Michael Dacre, Irene Kaplow, Kade Pettie, Peter Combs, and Hunter Fraser
eLife (2019)

2018

Realizing private and practical pharmacological collaboration
Brian Hie*, Hyunghoon Cho*, and Bonnie Berger
Science (2018)

2016

Pooled ChIP-seq links variation in transcription factor binding to complex disease risk
Ashley Tehranchi, Marsha Myrthil, Trevor Martin, Brian Hie, David Golan, and Hunter Fraser
Cell (2016)