目前~~苟且偷生~~在John Hopcroft实验室（数据挖掘与机器学习实验室），科研方向为AI4Sci，师从何琨教授。

GOAL: Awesome_docking

TO BE FINISHED

Paper Reading List

Newly Assigned

Name	Link
Structure prediction of protein-ligand complexes from sequence information with Umol	https://doi.org/10.1101/2023.11.03.565471
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking	https://arxiv.org/pdf/2210.01776.pdf
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?	https://arxiv.org/pdf/2302.07134.pdf
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences	https://arxiv.org/pdf/2308.05777.pdf
FABind: Fast and Accurate Protein-Ligand Binding	https://arxiv.org/pdf/2310.06763.pdf
Efficient and accurate large library ligand docking with KarmaDock	https://www.nature.com/articles/s43588-023-00511-5.pdf
Uni-Mol: A Universal 3D Molecular Representation Learning Framework	https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6402990d37e01856dc1d1581/original/uni-mol-a-universal-3d-molecular-representation-learning-framework.pdf

2023.11.27 UPDATED:

Archived List

Paper Reading List: https://www.notion.so/polowitty/c6199f9c39294210a76a5b47a63ca04b?v=9d8bd30310e345aab48b15b393944948

Name	Link
SchNet	https://arxiv.org/abs/1712.06113
Tensor field networks	https://arxiv.org/abs/1802.08219
N–BODY NETWORKS	https://arxiv.org/abs/1803.01588
DimeNet	https://arxiv.org/abs/2003.03123
Equivariant message passing for the prediction of tensorial properties and molecular spectra	https://arxiv.org/abs/2102.03150
SphereNet	https://arxiv.org/abs/2102.05013
Geometric Deep Learning	https://arxiv.org/abs/2104.13478
TORCHMD-NET	https://arxiv.org/abs/2202.02541
Geometrically Equivariant Graph Neural Networks — A Survey	https://arxiv.org/abs/2202.07230
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials	https://arxiv.org/abs/2205.06643
e3nn	https://arxiv.org/abs/2207.09453
Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations	https://arxiv.org/abs/2211.03226
Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution	https://arxiv.org/abs/2211.15903
SchNetPack 2.0	https://arxiv.org/abs/2212.05517
Rethinking SO(3)-equivariance with Bilinear Tensor Networks	https://arxiv.org/abs/2303.11288
TensorNet	https://arxiv.org/abs/2306.06482
EquiformerV2	https://arxiv.org/abs/2306.12059
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum systems	https://arxiv.org/abs/2307.08423
A functional approach to rotation equivariant non-linearities for Tensor Field Networks	https://openaccess.thecvf.com/content/CVPR2021/papers/Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper
ON THE UNIVERSALITY OF ROTATION EQUIVARIANT POINT CLOUD NETWORKS	https://arxiv.org/pdf/2010.02449
ICLR22 SEGNN	https://arxiv.org/abs/2110.02905
ICLR23 Equiformer	https://arxiv.org/abs/2206.11990
ICML20 Lorentz Group Equivariant Neural Network for Particle Physics	https://arxiv.org/abs/2006.04780
ICML21 E(n) Equivariant Graph Neural Networks	https://arxiv.org/abs/2102.09844
ICML22 Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces	https://arxiv.org/abs/2206.08362
ICML23 Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian	https://arxiv.org/abs/2306.04922
ICML23 eSCN
ICML23 On the Expressive Power of Geometric Graph Neural Networks	https://arxiv.org/abs/2301.09308
ICML23 Scaling Spherical CNNs	https://arxiv.org/abs/2306.05420
Nature Communications 22 NequIP	https://www.nature.com/articles/s41467-022-29939-5
Nature Communications 23 Allegro	https://www.nature.com/articles/s41467-023-36329-y
Nature Machine Intelligence Geometric deep learning on molecular representations	https://www.nature.com/articles/s42256-021-00418-8
NeurIPS18 3D Steerable CNNs	https://arxiv.org/abs/1807.02547
NeurIPS18 Clebsch–Gordan Nets	https://arxiv.org/abs/1806.09231
NeurIPS19 Cormorant	https://arxiv.org/abs/1906.04015
NeurIPS20 SE(3)-Transformers	https://arxiv.org/abs/2006.10503
NeurIPS21 GemNet	https://arxiv.org/abs/2106.08903
NeurIPS21 SE(3)-equivariant prediction of molecular wavefunctions and electronic densities	https://arxiv.org/abs/2106.02347
NeurIPS22 MACE	https://proceedings.neurips.cc/paper_files/paper/2022/hash/4a36c3c51af11ed9f34615b81edb5bbc-Abstract-Conference.html
NeurIPS22 So3krates	https://openreview.net/forum?id=tlUnxtAmcJq
s41586-023-06221-2 Scientific discovery in the age of artificial intelligence	https://www.nature.com/articles/s41586-023-06221-2

UPDATED: 2023.11.28

Course List

Name	Source
AI4SCUP	bilibili

FINISHED

Course List

Name	Source
CS224W: Machine Learning with Graphs	YouTube