10 citations · 10 across the 3 of their papers we have counts for
5 papers
Multi-Objective Protein Optimization via Modular Diffusion Steering
Meir Roketlishvili, Felix Huber, Wenda Chu +2
Protein engineering often requires improving several properties simultaneously, motivating the search for high-performing sequences that represent useful fitness tradeoffs. Discove…
On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
Trevor Chen, Ariel Dai, Jason Yang +8
Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, however, the goal is not to sample…
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
Steering Generative Models with Experimental Data for Protein Fitness Optimization
Jason Yang, Wenda Chu, Daniel Khalil +4
Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences. Recen…
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
Jason Yang, Ariane Mora, Shengchao Liu +4
Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are…