361 citations · 931 across the 14 of their papers we have counts for
15 papers
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Yilun Xu, Gabriele Corso, Tommi Jaakkola +2
Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, po…
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…
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…
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…
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…
Modeling Molecular Structures with Intrinsic Diffusion Models
Gabriele Corso
Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by…
Learning Graph Search Heuristics
Michal Pándy, Weikang Qiu, Gabriele Corso +4
Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology…
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…
Torsional Diffusion for Molecular Conformer Generation
Bowen Jing, Gabriele Corso, Jeffrey Chang +2
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-ar…
Subspace Diffusion Generative Models
Bowen Jing, Gabriele Corso, Renato Berlinghieri +1
Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire proce…
Neural Distance Embeddings for Biological Sequences
Gabriele Corso, Rex Ying, Michal Pándy +3
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.…
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…
Directional Graph Networks
Dominique Beaini, Saro Passaro, Vincent Létourneau +3
The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this lim…
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini +2
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on is…