Harrydirk41's Stars
XiangLi1999/Diffusion-LM
Diffusion-LM
google/jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
zongxin/Ferroflui_Droplet_MPI
learningmatter-mit/geom
GEOM: Energy-annotated molecular conformations
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
openbabel/openbabel
Open Babel is a chemical toolbox designed to speak the many languages of chemical data.
nwchemgit/nwchem-wiki
NWChem Documentation
soedinglab/MMseqs2
MMseqs2: ultra fast and sensitive search and clustering suite
torchmd/torchmd-protein-thermodynamics
Tutorials and data necessary to reproduce the results of publication Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
hsd1503/resnet1d
PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
luost26/RDE-PPI
:mountain: Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction (ICLR 2023)
salesforce/ctrl
Conditional Transformer Language Model for Controllable Generation
KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers
a Transformer-based neural network for generating highly optimized protein sequences called Regularized Latent Space Optimization (RELSO)
jessevig/bertviz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
ZhangGroup-MITChemistry/PCCG
Potential Contrasting Coarse-Graining
sokrypton/ColabFold
Making Protein folding accessible to all!
tiwarylab/alphafold2rave
harryjo97/GDSS
Official Code Repository for the paper "Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations" (ICML 2022)
ermongroup/CSDI
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"
bjing2016/EigenFold
EigenFold: Generative Protein Structure Prediction with Diffusion Models
aevans1/targetmeasure-mmap
Repo associated with paper "Computing committors via Mahalanobis diffusion maps with enhanced sampling data" by Luke Evans, Maria K. Cameron, and Pratyush Tiwary, https://arxiv.org/abs/2208.13772.
molmod/openmm-tutorial-msbs
OpenMM tutorial for the MSBS course
Peldom/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
yangkky/Machine-learning-for-proteins
Listing of papers about machine learning for proteins.
maccallumlab/meld
Modeling with limited data
gcorso/DiffDock
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
learningmatter-mit/GenZProt
e3nn/e3nn
A modular framework for neural networks with Euclidean symmetry
wwang2/CoarseGrainingVAE
Geometric super-resolution for molecular geometries
NVlabs/LSGM
The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)