arunraja-hub
PhD student @ Oxford Oxford Protein Informatics Group Core-contributor, GPT Code Clippy
Singapore
arunraja-hub's Stars
mlabonne/llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
thunlp/GNNPapers
Must-read papers on graph neural networks (GNN)
DeepGraphLearning/torchdrug
A powerful and flexible machine learning platform for drug discovery
PaddlePaddle/PaddleHelix
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
graphdeeplearning/graphtransformer
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
wehos/awesome-graph-transformer
Papers about graph transformers.
rampasek/GraphGPS
Recipe for a General, Powerful, Scalable Graph Transformer
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
divelab/AIRS
Artificial Intelligence Research for Science (AIRS)
MolecularAI/GraphINVENT
Graph neural networks for molecular design.
snap-stanford/neural-subgraph-learning-GNN
torchmd/torchmd-net
Training neural network potentials
kipoi/kipoi
Kipoi's model zoo API
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
pyt-team/TopoModelX
Topological Deep Learning
datamol-io/graphium
Graphium: Scaling molecular GNNs to infinity.
chaitjo/geometric-rna-design
gRNAde: Geometric Deep Learning for 3D RNA inverse design
MolecularAI/deep-molecular-optimization
Molecular optimization by capturing chemist’s intuition using the Seq2Seq with attention and the Transformer
VITA-Group/Deep_GCN_Benchmarking
[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
mengliu1998/awesome-expressive-gnn
A collection of papers studying/improving the expressiveness of graph neural networks (GNNs)
gbouritsas/GSN
Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxiv.org/abs/2006.09252
AstraZeneca/jazzy
Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules.
lucidrains/geometric-vector-perceptron
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021
mdsunivie/deeperwin
DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation. DeepErwin supports weight-sharing when optimizing wave functions for multiple nuclear geometries and the usage of pre-trained neural network weights to accelerate optimization.
AntonioLonga/Explaining-the-Explainers-in-Graph-Neural-Networks
Repository associated to the paper: "Explaining the Explainers in Graph Neural Networks: a Comparative Study"
davidbuterez/gnn-neural-readouts
schwallergroup/mhnn
Molecular Hypergraph Neural Network
KramerChristian/NonadditivityAnalysis
Code to analyze SAR datasets for Nonadditivity
MarkusFerdinandDablander/QSAR-activity-cliff-experiments
Exploring QSAR Models for Activity-Cliff Prediction
tgcsaba/graph2tens
Capturing Graphs with Hypo-Elliptic Diffusions