2.4k citations · 2.9k across the 7 of their papers we have counts for
7 papers
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…
Generative Humanization for Therapeutic Antibodies
Cade Gordon, Aniruddh Raghu, Peyton Greenside +1
Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanizat…
Inverse Protein Folding Using Deep Bayesian Optimization
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson +5
Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of…
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver +4
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption for drug design has been hindered by the discrete, high-dimen…
A Hierarchical Approach to Scaling Batch Active Search Over Structured Data
Vivek Myers, Peyton Greenside
Active search is the process of identifying high-value data points in a large and often high-dimensional parameter space that can be expensive to evaluate. Traditional active searc…
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, Anshul Kundaje
The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important…
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina +1
Note: This paper describes an older version of DeepLIFT. See https://arxiv.org/abs/1704.02685 for the newer version. Original abstract follows: The purported "black box" nature of…