HannesStark

DiffDock

Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

3DInfomax

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

CodonMPNN

dirichlet-flow-matching

EquiBind

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

FlowSite

Implementation of FlowSite and HarmonicFlow from the paper "Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design"

gnn-reinforcement-learning

Representing robots as graphs for reinforcement-learning in PyBullet locomotion environments.

protein-localization

Using Transformer protein embeddings with a linear attention mechanism to make SOTA de-novo predictions for the subcellular location of proteins :microscope:

SMPL-NeRF

Embed human pose information into neural radiance fields (NeRF) to render images of humans in desired poses :running: from novel views

DiffLinker

DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

HannesStark/EquiBind

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

HannesStark/3DInfomax

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

HannesStark/FlowSite

Implementation of FlowSite and HarmonicFlow from the paper "Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design"

HannesStark/dirichlet-flow-matching

HannesStark/SMPL-NeRF

Embed human pose information into neural radiance fields (NeRF) to render images of humans in desired poses :running: from novel views

HannesStark/protein-localization

Using Transformer protein embeddings with a linear attention mechanism to make SOTA de-novo predictions for the subcellular location of proteins :microscope:

HannesStark/gnn-reinforcement-learning

Representing robots as graphs for reinforcement-learning in PyBullet locomotion environments.

HannesStark/CodonMPNN

HannesStark/hannes-stark

Code for my website built with Angular and running on GitHub Pages.

HannesStark/GNN-primer

HannesStark/attention-to-binding-sites

Unsupervised method for binding site prediction using attention patterns of protein language models.

HannesStark/audioImprovement

Removing background noise from clips of speech and improving audio quality (PyTorch)

HannesStark/molecule-ELECTRA

Pre-train and evaluate Graph Neural Networks or Transformers on molecules with the ELECTRA method.

HannesStark/genie

HannesStark/bachelorThesis

TensorFlow code and LaTex for Bachelor Thesis: Understanding Variational Autoencoders' Latent Representations of Remote Sensing Images :earth_africa:

HannesStark/e3nn

A modular framework for neural networks with Euclidean symmetry

HannesStark/ec-number-prediction

Using similarity in embedding space for predicting EC numbers

HannesStark/HannesStark

HannesStark/logag

HannesStark/ogb

Benchmark datasets, data loaders, and evaluators for graph machine learning

HannesStark/se3-transformer-public

code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503

HannesStark/testtt_anonymous

HannesStark/bayesian-deep-learning

HannesStark/bio_embeddings

Get protein embeddings from protein sequences

HannesStark/chroma

HannesStark/DIG

A library for graph deep learning research

HannesStark/GeoMol

HannesStark/images

HannesStark/SAN

HannesStark/test