6 citations · 10 across the 4 of their papers we have counts for
5 papers
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
Felix Teufel, Aaron W. Kollasch, Yining Huang +4
Accurately predicting protein fitness with minimal experimental data is a persistent challenge in protein engineering. We introduce PRIMO (PRotein In-context Mutation Oracle), a tr…
MotifBench: A standardized protein design benchmark for motif-scaffolding problems
Zhuoqi Zheng, Bo Zhang, Kieran Didi +5
The motif-scaffolding problem is a central task in computational protein design: Given the coordinates of atoms in a geometry chosen to confer a desired biochemical function (a mot…
Towards deep learning sequence-structure co-generation for protein design
Chentong Wang, Sarah Alamdari, Carles Domingo-Enrich +2
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majorit…
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Chenqing Hua, Yong Liu, Dinghuai Zhang +6
Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or prote…
Machine Learning for Protein Engineering
Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann +3
Directed evolution of proteins has been the most effective method for protein engineering. However, a new paradigm is emerging, fusing the library generation and screening approach…