replace github address (#246)

Co-authored-by: dp <[email protected]>
deepmodeling · Jul 4, 2024 · 79853f9 · 79853f9
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diff --git a/README.md b/README.md
@@ -20,7 +20,7 @@ Shortcuts
 Uni-Mol: A Universal 3D Molecular Representation Learning Framework
 -------------------------------------------------------------------
 
-[[Paper](https://openreview.net/forum?id=6K2RM6wVqKu)], [[Uni-Mol Docking Colab](https://colab.research.google.com/github/dptech-corp/Uni-Mol/blob/main/unimol/notebooks/unimol_binding_pose_demo.ipynb)]
+[[Paper](https://openreview.net/forum?id=6K2RM6wVqKu)], [[Uni-Mol Docking Colab](https://colab.research.google.com/github/deepmodeling/Uni-Mol/blob/main/unimol/notebooks/unimol_binding_pose_demo.ipynb)]
 
 Authors: Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke 
 
@@ -147,4 +147,4 @@ Please kindly cite our papers if you use the data/code/model.
 License
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/dptech-corp/Uni-Mol/blob/main/LICENSE) for additional details.
+This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/deepmodeling/Uni-Mol/blob/main/LICENSE) for additional details.
diff --git a/unimol/README.md b/unimol/README.md
@@ -1,7 +1,7 @@
 Uni-Mol: A Universal 3D Molecular Representation Learning Framework 
 ===================================================================
 
-[[Paper](https://openreview.net/forum?id=6K2RM6wVqKu)], [[Uni-Mol Docking Colab](https://colab.research.google.com/github/dptech-corp/Uni-Mol/blob/main/unimol/notebooks/unimol_binding_pose_demo.ipynb)]
+[[Paper](https://openreview.net/forum?id=6K2RM6wVqKu)], [[Uni-Mol Docking Colab](https://colab.research.google.com/github/deepmodeling/Uni-Mol/blob/main/unimol/notebooks/unimol_binding_pose_demo.ipynb)]
 
 Authors: Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke 
 
@@ -63,23 +63,23 @@ Uni-Mol's pretrained model weights
 
 | Model                     | File Size  |Update Date | Download Link                                                | 
 |--------------------------|------------| ------------|--------------------------------------------------------------|
-| molecular pretrain       | 181MB   | Aug 17 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mol_pre_no_h_220816.pt                |
-| pocket pretrain          | 181MB   | Aug 17 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt                  |
+| molecular pretrain       | 181MB   | Aug 17 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/mol_pre_no_h_220816.pt                |
+| pocket pretrain          | 181MB   | Aug 17 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt                  |
 
 
 Uni-Mol's finetuned model weights
 ----------------------------------
 
 | Model                                           | File Size| Update Date| Download Link                                                     | 
 |-------------------------------------------------|---------| -----------|--------------------------------------------------------------------|
-| molecular conformation generation (qm9)         | 181MB   | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/qm9_220908.pt    |
-| molecular conformation generation (drugs)       | 181MB   | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/drugs_220908.pt  |
-| Protein-ligand binding pose prediction          | 415MB   | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt      |
+| molecular conformation generation (qm9)         | 181MB   | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/qm9_220908.pt    |
+| molecular conformation generation (drugs)       | 181MB   | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/drugs_220908.pt  |
+| Protein-ligand binding pose prediction          | 415MB   | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt      |
 
 
 Dependencies
 ------------
- - [Uni-Core](https://github.com/dptech-corp/Uni-Core), check its [Installation Documentation](https://github.com/dptech-corp/Uni-Core#installation).
+ - [Uni-Core](https://github.com/deepmodeling/Uni-Core), check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
  - rdkit==2022.9.3, install via `pip install rdkit-pypi==2022.9.3`
 
 To use GPUs within docker you need to [install nvidia-docker-2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) first. Use the following command to pull the docker image:
@@ -268,7 +268,7 @@ For ClinTox, Tox21, ToxCast, SIDER, HIV, PCBA and MUV, we set `loss_func=multi_t
 For ESOL, FreeSolv and Lipo, we set `loss_func=finetune_mse`.
 For QM7, QM8 and QM9, we set `loss_func=finetune_smooth_mae`.
 
-**NOTE**: Our first version of the molecular pretraining ran with **all hydrogen** pretrained model, and above hyper-parameters are also for **all hydrogen** pretrained model. You can download the [all hydrogen model parameter](https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mol_pre_all_h_220816.pt) here, and use it with `only_polar=-1` to reproduce our results. The performance of pretraining model with **no hydrogen** is very close to the **all hydrogen** one in molecular property prediction. We will update the hyperparameters for the no hydrogen version later.
+**NOTE**: Our first version of the molecular pretraining ran with **all hydrogen** pretrained model, and above hyper-parameters are also for **all hydrogen** pretrained model. You can download the [all hydrogen model parameter](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/mol_pre_all_h_220816.pt) here, and use it with `only_polar=-1` to reproduce our results. The performance of pretraining model with **no hydrogen** is very close to the **all hydrogen** one in molecular property prediction. We will update the hyperparameters for the no hydrogen version later.
 
 **NOTE**: For reproduce, you can do the validation on test set while training, with `--valid-subset valid` changing to `--valid-subset valid,test`. The model selection is still based on the performance of the valid set. It is controlled by `--best-checkpoint-metric $metric`.
 
@@ -537,4 +537,4 @@ Please kindly cite this paper if you use the data/code/model.
 License
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/dptech-corp/Uni-Mol/blob/main/LICENSE) for additional details.
+This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/deepmodeling/Uni-Mol/blob/main/LICENSE) for additional details.
diff --git a/unimol/notebooks/unimol_binding_pose_demo.ipynb b/unimol/notebooks/unimol_binding_pose_demo.ipynb
@@ -8,14 +8,14 @@
       "source": [
         "# Uni-Mol Binding Pose Prediction Colab\n",
         "\n",
-        "This Colab notebook provides an online runnable version of [Uni-Mol](https://github.com/dptech-corp/Uni-Mol/) binding pose prediction (short for \"docking\" in the following) with custom settings.\n",
+        "This Colab notebook provides an online runnable version of [Uni-Mol](https://github.com/deepmodeling/Uni-Mol/) binding pose prediction (short for \"docking\" in the following) with custom settings.\n",
         "Uni-Mol docking is very fast in dozens of seconds. \n",
         "\n",
         "Please note that this Colab notebook is not a finished product and is provided as an early-access prototype. It is provided for theoretical modeling only and caution should be exercised in its use. \n",
         "\n",
         "**Licenses**\n",
         "\n",
-        "This Colab uses the [Uni-Mol model parameters](https://github.com/dptech-corp/Uni-Mol/LICENSE) and its outputs are under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can find details at: https://creativecommons.org/licenses/by/4.0/legalcode. The Colab is provided under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\n",
+        "This Colab uses the [Uni-Mol model parameters](https://github.com/deepmodeling/Uni-Mol/LICENSE) and its outputs are under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can find details at: https://creativecommons.org/licenses/by/4.0/legalcode. The Colab is provided under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\n",
         "\n",
         "\n",
         "**Citations**\n",
@@ -38,10 +38,10 @@
         "%%bash\n",
         "#@title Install dependencies\n",
         "\n",
-        "GIT_REPO='https://github.com/dptech-corp/Uni-Mol'\n",
-        "UNICORE_URL='https://github.com/dptech-corp/Uni-Core/releases/download/0.0.2/unicore-0.0.1+cu116torch1.13.1-cp39-cp39-linux_x86_64.whl'\n",
-        "DOCKING_DATA_URL='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
-        "DOCKING_WEIGHT_URL='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt'\n",
+        "GIT_REPO='https://github.com/deepmodeling/Uni-Mol'\n",
+        "UNICORE_URL='https://github.com/deepmodeling/Uni-Core/releases/download/0.0.2/unicore-0.0.1+cu116torch1.13.1-cp39-cp39-linux_x86_64.whl'\n",
+        "DOCKING_DATA_URL='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
+        "DOCKING_WEIGHT_URL='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt'\n",
         "if [ ! -f UNIMOL_READY ]; then\n",
         "  wget -q ${UNICORE_URL} \n",
         "  pip3 -q install \"unicore-0.0.1+cu116torch1.13.1-cp39-cp39-linux_x86_64.whl\" \n",

diff --git a/unimol/notebooks/unimol_pocket_repr_demo.ipynb b/unimol/notebooks/unimol_pocket_repr_demo.ipynb
@@ -40,8 +40,8 @@
    "outputs": [],
    "source": [
     "%%bash\n",
-    "pocket_data_url='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
-    "pocket_weight_url='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt'\n",
+    "pocket_data_url='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
+    "pocket_weight_url='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt'\n",
     "wget -q ${pocket_data_url}\n",
     "tar -xzf \"CASF-2016.tar.gz\"\n",
     "wget -q ${pocket_weight_url}"

diff --git a/unimol/requirements.txt b/unimol/requirements.txt
@@ -1 +1 @@
-git+git://github.com/dptech-crop/Uni-Core.git@stable#egg=Uni-Core
+git+git://github.com/deepmodeling/Uni-Core.git@stable#egg=Uni-Core
diff --git a/unimol/setup.py b/unimol/setup.py
@@ -10,7 +10,7 @@
     author="DP Technology",
     author_email="[email protected]",
     license="The MIT License",
-    url="https://github.com/dptech-corp/Uni-Mol",
+    url="https://github.com/deepmodeling/Uni-Mol",
     packages=find_packages(
         exclude=["scripts", "tests", "example_data", "docker", "figure"]
     ),

diff --git a/unimol_docking_v2/README.md b/unimol_docking_v2/README.md
@@ -12,7 +12,7 @@ Service of Uni-Mol Docking V2 is avaiable at https://bohrium.dp.tech/apps/unimol
 
 Dependencies
 ------------
- - [Uni-Core](https://github.com/dptech-corp/Uni-Core), check its [Installation Documentation](https://github.com/dptech-corp/Uni-Core#installation).
+ - [Uni-Core](https://github.com/deepmodeling/Uni-Core), check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
  - rdkit==2022.9.3, install via `pip install rdkit-pypi==2022.9.3 -i https://pypi.tuna.tsinghua.edu.cn/simple/ --trusted-host pypi.tuna.tsinghua.edu.cn`
   - biopandas==0.4.1, install via `pip install biopandas`
 
@@ -21,7 +21,7 @@ Data
 | Data                     | File Size  | Update Date | Download Link                                                                                                             | 
 |--------------------------|------------| ----------- |---------------------------------------------------------------------------------------------------------------------------|
 | Raw training data       | 4.95GB   | May 14 2024 |https://zenodo.org/records/11191555     |
-| Posebusters and Astex   | 8.2MB   | Nov 16 2023 |https://github.com/dptech-corp/Uni-Mol/files/13352676/eval_sets.zip     |
+| Posebusters and Astex   | 8.2MB   | Nov 16 2023 |https://github.com/deepmodeling/Uni-Mol/files/13352676/eval_sets.zip     |
 
 
 Note that we use the `Posebusters V1` (428 datapoints, released in August 2023). For the latest version, please refer to [Posebusters repo](https://github.com/maabuu/posebusters).
@@ -113,4 +113,4 @@ Please kindly cite this paper if you use the data/code/model.
 License
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/dptech-corp/Uni-Mol/blob/main/LICENSE) for additional details.
+This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/deepmodeling/Uni-Mol/blob/main/LICENSE) for additional details.
diff --git a/unimol_plus/README.md b/unimol_plus/README.md
@@ -12,21 +12,21 @@ In the [PCQM4MV2](https://ogb.stanford.edu/docs/lsc/leaderboards/#pcqm4mv2) benc
 
 | Model Settings   | # Layers   | # Param.    | Validation MAE   | Model Checkpoint | 
 |------------------|------------| ----------- |------------------|------------------|
-| Uni-Mol+         |     12     |   52.4 M    | 0.0696           | [link](https://github.com/dptech-corp/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_base.pt)          |
-| Uni-Mol+ Large   |     18     |   77 M      | 0.0693           | [link](https://github.com/dptech-corp/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_large.pt)         |
-| Uni-Mol+ Small   |      6     |   27.7 M    | 0.0714           | [link](https://github.com/dptech-corp/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_small.pt)         |
+| Uni-Mol+         |     12     |   52.4 M    | 0.0696           | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_base.pt)          |
+| Uni-Mol+ Large   |     18     |   77 M      | 0.0693           | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_large.pt)         |
+| Uni-Mol+ Small   |      6     |   27.7 M    | 0.0714           | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_small.pt)         |
 
 
 In the [OC20](https://opencatalystproject.org/leaderboard.html) IS2RE  benchmark, Uni-Mol+ outperforms previous SOTA methods by a large margin.
 
 | Model Settings   | # Layers   | # Param.    | Validation Mean MAE  | Test Mean MAE  | Model Checkpoint | 
 |------------------|------------| ----------- |----------------------|----------------|------------------|
-| Uni-Mol+         |     12     |  48.6 M     | 0.4088               | 0.4143         | [link](https://github.com/dptech-corp/Uni-Mol/releases/download/v0.2/unimol_plus_oc20_base.pt)          |
+| Uni-Mol+         |     12     |  48.6 M     | 0.4088               | 0.4143         | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_oc20_base.pt)          |
 
 
 Dependencies
 ------------
- - [Uni-Core](https://github.com/dptech-corp/Uni-Core) with pytorch > 2.0.0, check its [Installation Documentation](https://github.com/dptech-corp/Uni-Core#installation).
+ - [Uni-Core](https://github.com/deepmodeling/Uni-Core) with pytorch > 2.0.0, check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
  - rdkit==2022.09.3, install via `pip install rdkit==2022.09.3`
  - numba and pandas, install via `pip install numba pandas`
 
@@ -128,4 +128,4 @@ Please kindly cite this paper if you use the data/code/model.
 License
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/dptech-corp/Uni-Mol/blob/main/LICENSE) for additional details.
+This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/deepmodeling/Uni-Mol/blob/main/LICENSE) for additional details.
diff --git a/unimol_plus/setup.py b/unimol_plus/setup.py
@@ -10,7 +10,7 @@
     author="DP Technology",
     author_email="[email protected]",
     license="The MIT License",
-    url="https://github.com/dptech-corp/Uni-Mol",
+    url="https://github.com/deepmodeling/Uni-Mol",
     packages=find_packages(
         exclude=["scripts", "tests", "example_data", "docker", "figure"]
     ),

diff --git a/unimol_tools/README.md b/unimol_tools/README.md
@@ -31,7 +31,7 @@ pip install huggingface_hub
 pip install -r requirements.txt
 
 ## Clone repository
-git clone https://github.com/dptech-corp/Uni-Mol.git
+git clone https://github.com/deepmodeling/Uni-Mol.git
 cd Uni-Mol/unimol_tools
 
 ## Install

diff --git a/unimol_tools/setup.py b/unimol_tools/setup.py
@@ -10,7 +10,7 @@
     author="DP Technology",
     author_email="[email protected]",
     license="The MIT License",
-    url="https://github.com/dptech-corp/Uni-Mol/unimol_tools",
+    url="https://github.com/deepmodeling/Uni-Mol/unimol_tools",
     packages=find_packages(
         where='.',
         exclude=[