Search Results for author:
Found 10 papers, 7 papers with code
3DReact: Geometric deep learning for chemical reactions
We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion).
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
Most traditional and deep learning methods for docking have focused mainly on binary docking, following either a search-based, regression-based, or generative modeling paradigm.
Aligned Diffusion Schrödinger Bridges
Diffusion Schr\"odinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points.
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Inferring causal structures from experimentation is a central task in many domains.
Multi-Scale Representation Learning on Proteins
This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence.
Learning Graph Models for Template-Free Retrosynthesis
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Ranked #7 on
Single-step retrosynthesis
on USPTO-50k
Learning Graph Models for Retrosynthesis Prediction
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively.
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture.
