27 citations · 57 across the 9 of their papers we have counts for
10 papers
Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo
Luran Wang, Linrui Ma, Hannes Stärk +1
Inference-time steering enables pretrained diffusion models to satisfy new constraints without full retraining. However, specificity-aware generation is difficult: repelling sample…
ProxelGen: Generating Proteins as 3D Densities
Felix Faltings, Hannes Stark, Regina Barzilay +1
We develop ProxelGen, a protein structure generative model that operates on 3D densities as opposed to the prevailing 3D point cloud representations. Representing proteins as voxel…
Protein FID: Improved Evaluation of Protein Structure Generative Models
Felix Faltings, Hannes Stark, Tommi Jaakkola +1
Protein structure generative models have seen a recent surge of interest, but meaningfully evaluating them computationally is an active area of research. While current metrics have…
Hierarchical protein backbone generation with latent and structure diffusion
Jason Yim, Marouane Jaakik, Ge Liu +5
We propose a hierarchical protein backbone generative model that separates coarse and fine-grained details. Our approach called LSD consists of two stages: sampling latents which a…
Predicting sub-population specific viral evolution
Wenxian Shi, Menghua Wu, Regina Barzilay
Forecasting the change in the distribution of viral variants is crucial for therapeutic design and disease surveillance. This task poses significant modeling challenges due to the…
Identifying biological perturbation targets through causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy +2
Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and intervent…
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Gabriele Corso, Arthur Deng, Benjamin Fry +3
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Exi…
Dirichlet Flow Matching with Applications to DNA Sequence Design
Hannes Stark, Bowen Jing, Chenyu Wang +4
Discrete diffusion or flow models could enable faster and more controllable sequence generation than autoregressive models. We show that naïve linear flow matching on the simplex i…
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Andrew Campbell, Jason Yim, Regina Barzilay +2
Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provid…
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…