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Found 17 papers, 11 papers with code
EquiReact: An equivariant neural network for chemical reactions
Equivariant neural networks have considerably improved the accuracy and data-efficiency of predictions of molecular properties.
Transition Path Sampling with Boltzmann Generator-based MCMC Moves
Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery.
Reproducible image-based profiling with Pycytominer
Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images.
Unbalanced Diffusion Schrödinger Bridge
Schr\"odinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems.
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.
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
However, the usual formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes (e. g., cell proliferation or death) between measurements.
Supervised Training of Conditional Monge Maps
To account for that context in OT estimation, we introduce CondOT, a multi-task approach to estimate a family of OT maps conditioned on a context variable, using several pairs of measures $\left(\mu_i, \nu_i\right)$ tagged with a context label $c_i$.
Invariant Causal Mechanisms through Distribution Matching
Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks.
Multi-Scale Representation Learning on Proteins
This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence.
The Schrödinger Bridge between Gaussian Measures has a Closed Form
The static optimal transport $(\mathrm{OT})$ problem between Gaussians seeks to recover an optimal map, or more generally a coupling, to morph a Gaussian into another.
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms.
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e. g. drug design or protein engineering.
Proximal Optimal Transport Modeling of Population Dynamics
We propose to model these trajectories as collective realizations of a causal Jordan-Kinderlehrer-Otto (JKO) flow of measures: The JKO scheme posits that the new configuration taken by a population at time $t+1$ is one that trades off an improvement, in the sense that it decreases an energy, while remaining close (in Wasserstein distance) to the previous configuration observed at $t$.
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 #5 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.
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety.
Studying Invariances of Trained Convolutional Neural Networks
Convolutional Neural Networks (CNNs) define an exceptionally powerful class of models for image classification, but the theoretical background and the understanding of how invariances to certain transformations are learned is limited.
