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‎README.md‎

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Uni-Mol: A Universal 3D Molecular Representation Learning Framework
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-------------------------------------------------------------------
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[[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)]
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[[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)]
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Authors: Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke
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License
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-------
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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.
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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.

‎unimol/README.md‎

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Uni-Mol: A Universal 3D Molecular Representation Learning Framework
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===================================================================
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[[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)]
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[[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)]
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Authors: Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke
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| Model | File Size |Update Date | Download Link |
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|--------------------------|------------| ------------|--------------------------------------------------------------|
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| molecular pretrain | 181MB | Aug 17 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mol_pre_no_h_220816.pt |
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| pocket pretrain | 181MB | Aug 17 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt |
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| molecular pretrain | 181MB | Aug 17 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/mol_pre_no_h_220816.pt |
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| pocket pretrain | 181MB | Aug 17 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt |
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Uni-Mol's finetuned model weights
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----------------------------------
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| Model | File Size| Update Date| Download Link |
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|-------------------------------------------------|---------| -----------|--------------------------------------------------------------------|
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| molecular conformation generation (qm9) | 181MB | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/qm9_220908.pt |
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| molecular conformation generation (drugs) | 181MB | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/drugs_220908.pt |
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| Protein-ligand binding pose prediction | 415MB | Sep 8 2022 |https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt |
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| molecular conformation generation (qm9) | 181MB | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/qm9_220908.pt |
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| molecular conformation generation (drugs) | 181MB | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/drugs_220908.pt |
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| Protein-ligand binding pose prediction | 415MB | Sep 8 2022 |https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt |
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Dependencies
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------------
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- [Uni-Core](https://github.com/dptech-corp/Uni-Core), check its [Installation Documentation](https://github.com/dptech-corp/Uni-Core#installation).
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- [Uni-Core](https://github.com/deepmodeling/Uni-Core), check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
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- rdkit==2022.9.3, install via `pip install rdkit-pypi==2022.9.3`
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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:
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For ESOL, FreeSolv and Lipo, we set `loss_func=finetune_mse`.
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For QM7, QM8 and QM9, we set `loss_func=finetune_smooth_mae`.
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**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.
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**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.
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**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`.
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License
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-------
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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.
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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.

‎unimol/notebooks/unimol_binding_pose_demo.ipynb‎

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"source": [
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"# Uni-Mol Binding Pose Prediction Colab\n",
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"\n",
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"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",
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"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",
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"Uni-Mol docking is very fast in dozens of seconds. \n",
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"\n",
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"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",
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"\n",
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"**Licenses**\n",
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"\n",
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"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",
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"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",
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"\n",
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"\n",
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"**Citations**\n",
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"%%bash\n",
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"#@title Install dependencies\n",
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"\n",
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"GIT_REPO='https://github.com/dptech-corp/Uni-Mol'\n",
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"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",
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"DOCKING_DATA_URL='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
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"DOCKING_WEIGHT_URL='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt'\n",
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"GIT_REPO='https://github.com/deepmodeling/Uni-Mol'\n",
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"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",
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"DOCKING_DATA_URL='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
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"DOCKING_WEIGHT_URL='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/binding_pose_220908.pt'\n",
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"if [ ! -f UNIMOL_READY ]; then\n",
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" wget -q ${UNICORE_URL} \n",
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" pip3 -q install \"unicore-0.0.1+cu116torch1.13.1-cp39-cp39-linux_x86_64.whl\" \n",

‎unimol/notebooks/unimol_pocket_repr_demo.ipynb‎

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"%%bash\n",
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"pocket_data_url='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
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"pocket_weight_url='https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt'\n",
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"pocket_data_url='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/CASF-2016.tar.gz'\n",
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"pocket_weight_url='https://github.com/deepmodeling/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt'\n",
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"wget -q ${pocket_data_url}\n",
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"tar -xzf \"CASF-2016.tar.gz\"\n",
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"wget -q ${pocket_weight_url}"

‎unimol/requirements.txt‎

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git+git://github.com/dptech-crop/Uni-Core.git@stable#egg=Uni-Core
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git+git://github.com/deepmodeling/Uni-Core.git@stable#egg=Uni-Core

‎unimol/setup.py‎

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author="DP Technology",
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author_email="unimol@dp.tech",
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license="The MIT License",
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url="https://github.com/dptech-corp/Uni-Mol",
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url="https://github.com/deepmodeling/Uni-Mol",
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packages=find_packages(
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‎unimol_docking_v2/README.md‎

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- [Uni-Core](https://github.com/dptech-corp/Uni-Core), check its [Installation Documentation](https://github.com/dptech-corp/Uni-Core#installation).
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- [Uni-Core](https://github.com/deepmodeling/Uni-Core), check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
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- 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`
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- biopandas==0.4.1, install via `pip install biopandas`
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| Data | File Size | Update Date | Download Link |
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| Raw training data | 4.95GB | May 14 2024 |https://zenodo.org/records/11191555 |
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| Posebusters and Astex | 8.2MB | Nov 16 2023 |https://github.com/dptech-corp/Uni-Mol/files/13352676/eval_sets.zip |
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| Posebusters and Astex | 8.2MB | Nov 16 2023 |https://github.com/deepmodeling/Uni-Mol/files/13352676/eval_sets.zip |
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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).
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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.
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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.

‎unimol_plus/README.md‎

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| Model Settings | # Layers | # Param. | Validation MAE | Model Checkpoint |
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| 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) |
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| 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) |
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| 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) |
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| Uni-Mol+ | 12 | 52.4 M | 0.0696 | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_base.pt) |
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| Uni-Mol+ Large | 18 | 77 M | 0.0693 | [link](https://github.com/deepmodeling/Uni-Mol/releases/download/v0.2/unimol_plus_pcq_large.pt) |
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| 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) |
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In the [OC20](https://opencatalystproject.org/leaderboard.html) IS2RE benchmark, Uni-Mol+ outperforms previous SOTA methods by a large margin.
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| Model Settings | # Layers | # Param. | Validation Mean MAE | Test Mean MAE | Model Checkpoint |
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| 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) |
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| 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) |
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- [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).
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- [Uni-Core](https://github.com/deepmodeling/Uni-Core) with pytorch > 2.0.0, check its [Installation Documentation](https://github.com/deepmodeling/Uni-Core#installation).
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- numba and pandas, install via `pip install numba pandas`
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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.
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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.

‎unimol_plus/setup.py‎

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url="https://github.com/deepmodeling/Uni-Mol",
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‎unimol_tools/README.md‎

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git clone https://github.com/deepmodeling/Uni-Mol.git
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## Install

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