828 citations · 5.6k across the 171 of their papers we have counts for
25 papers · 1 filter
Fast non-autoregressive inverse folding with discrete diffusion
John J. Yang, Jason Yim, Regina Barzilay +1
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this es…
Risk-Controlling Model Selection via Guided Bayesian Optimization
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is t…
Correcting Diffusion Generation through Resampling
Yujian Liu, Yang Zhang, Tommi Jaakkola +1
Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth image…
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
Bowen Jing, Tommi Jaakkola, Bonnie Berger
Molecular docking is critical to structure-based virtual screening, yet the throughput of such workflows is limited by the expensive optimization of scoring functions involved in m…
Removing Biases from Molecular Representations via Information Maximization
Chenyu Wang, Sharut Gupta, Caroline Uhler +1
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the…
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Gabriele Corso, Yilun Xu, Valentin de Bortoli +2
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sample…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Xiang Fu, Tian Xie, Andrew S. Rosen +2
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modula…
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
Hannes Stärk, Bowen Jing, Regina Barzilay +1
A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactf…
Compositional Sculpting of Iterative Generative Processes
Timur Garipov, Sebastiaan De Peuter, Ge Yang +3
High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composi…
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Improving Protein Optimization with Smoothed Fitness Landscapes
Andrew Kirjner, Jason Yim, Raman Samusevich +4
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of…
Restart Sampling for Improving Generative Processes
Yilun Xu, Mingyang Deng, Xiang Cheng +3
Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plat…
Conformal Language Modeling
Victor Quach, Adam Fisch, Tal Schuster +4
We propose a novel approach to conformal prediction for generative language models (LMs). Standard conformal prediction produces prediction sets -- in place of single predictions -…
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov +6
Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulat…
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
Guanhua Zhang, Jiabao Ji, Yang Zhang +3
Image inpainting refers to the task of generating a complete, natural image based on a partially revealed reference image. Recently, many research interests have been focused on ad…
GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu +2
Since diffusion models (DM) and the more recent Poisson flow generative models (PFGM) are inspired by physical processes, it is reasonable to ask: Can physical processes offer addi…
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Bowen Jing, Ezra Erives, Peter Pao-Huang +3
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensem…
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira +8
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique…
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
Yilun Xu, Shangyuan Tong, Tommi Jaakkola
Diffusion models generate samples by reversing a fixed forward diffusion process. Despite already providing impressive empirical results, these diffusion models algorithms can be f…
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian +3
We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generati…
SE(3) diffusion model with application to protein backbone generation
Jason Yim, Brian L. Trippe, Valentin De Bortoli +4
The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over ri…