6 papers
A Unification of Discrete, Gaussian, and Simplicial Diffusion
Nuria Alina Chandra, Yucen Lily Li, Alan N. Amin +5
To model discrete sequences such as DNA, proteins, and language using diffusion, practitioners must choose between three major methods: diffusion in discrete space, Gaussian diffus…
A Diffusion Model to Shrink Proteins While Maintaining Their Function
Ethan Baron, Alan N. Amin, Ruben Weitzman +2
Many proteins useful in modern medicine or bioengineering are challenging to make in the lab, fuse with other proteins in cells, or deliver to tissues in the body, because their se…
Is Sequence Information All You Need for Bayesian Optimization of Antibodies?
Sebastian W. Ober, Calvin McCarter, Aniruddh Raghu +4
Bayesian optimization is a natural candidate for the engineering of antibody therapeutic properties, which is often iterative and expensive. However, finding the optimal choice of…
Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
Alan N. Amin, Nate Gruver, Andrew Gordon Wilson
Discrete diffusion models, like continuous diffusion models, generate high-quality samples by gradually undoing noise applied to datapoints with a Markov process. Gradual generatio…
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra
Alan N. Amin, Andres Potapczynski, Andrew Gordon Wilson
To understand how genetic variants in human genomes manifest in phenotypes -- traits like height or diseases like asthma -- geneticists have sequenced and measured hundreds of thou…
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
Alan Nawzad Amin, Nate Gruver, Yilun Kuang +6
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or b…