361 citations · 619 across the 15 of their papers we have counts for
16 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…
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…
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
Hannes Stark, Bowen Jing, Tomas Geffner +4
We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semant…
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
Generative Modeling of Molecular Dynamics Trajectories
Bowen Jing, Hannes Stärk, Tommi Jaakkola +1
Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-b…
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…
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…
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…
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…
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Xiangyu Zhao, Hannes Stärk, Dominique Beaini +2
It has been increasingly demanding to develop reliable methods to evaluate the progress of Graph Neural Network (GNN) research for molecular representation learning. Existing GNN b…
Generalized Laplacian Positional Encoding for Graph Representation Learning
Sohir Maskey, Ali Parviz, Maximilian Thiessen +3
Graph neural networks (GNNs) are the primary tool for processing graph-structured data. Unfortunately, the most commonly used GNNs, called Message Passing Neural Networks (MPNNs) s…
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing +2
Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat…
Graph Anisotropic Diffusion
Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk +1
Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features. Such a process is isotropic and th…
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik +2
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applicat…
3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso +4
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned mod…
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Zekarias T. Kefato, Sarunas Girdzijauskas, Hannes Stärk
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning h…