Hannes Stärk

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

HannesStark

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

HannesStark

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

HannesStark

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

HannesStark

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

HannesStark

HannesStark

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

HannesStark