--- a/README.md
+++ b/README.md
@@ -1,185 +1,178 @@
-[](https://torchdrug.ai/)
-<h1 align="center">
- with
- <a href="https://torchprotein.ai/">
- <img src="asset/torchprotein_logo_tight.svg" alt="TorchProtein" style="height:26px" />
- </a>
-</h1>
-
-[](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true)
-[](https://github.com/DeepGraphLearning/torchdrug/blob/master/CONTRIBUTING.md)
-[](https://github.com/DeepGraphLearning/torchdrug/blob/master/LICENSE)
-[](https://pypi.org/project/torchdrug/)
-[](https://twitter.com/DrugTorch)
-
-[Docs] | [Tutorials] | [Benchmarks] | [Papers Implemented]
-
-[Docs]: https://deepgraphlearning.github.io/torchdrug-site/docs
-[Tutorials]: https://deepgraphlearning.github.io/torchdrug-site/docs/tutorials
-[Benchmarks]: https://deepgraphlearning.github.io/torchdrug-site/docs/benchmark
-[Papers Implemented]: https://deepgraphlearning.github.io/torchdrug-site/docs/paper
-
-TorchDrug is a [PyTorch]-based machine learning toolbox designed for several purposes.
-
-- Easy implementation of graph operations in a PyTorchic style with GPU support
-- Being friendly to practitioners with minimal knowledge about drug discovery
-- Rapid prototyping of machine learning research
-
-[PyTorch]: https://pytorch.org/
-
-Installation
-------------
-
-TorchDrug can be installed on either Linux, Windows or macOS. It is compatible with
-3.7 <= Python <= 3.10 and PyTorch >= 1.8.0.
-
-### From Conda ###
-
-```bash
-conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg
-```
-
-### From Pip ###
-
-```bash
-pip install torch==1.9.0
-pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html
-pip install torchdrug
-```
-
-To install `torch-scatter` for other PyTorch or CUDA versions, please see the
-instructions in https://github.com/rusty1s/pytorch_scatter
-
-### From Source ###
-
-```bash
-git clone https://github.com/DeepGraphLearning/torchdrug
-cd torchdrug
-pip install -r requirements.txt
-python setup.py install
-```
-
-### Windows (PowerShell) ###
-
-We need to first install the build tools for Visual Studio. We then install the
-following modules in PowerShell.
-
-```powershell
-Install-Module Pscx -AllowClobber
-Install-Module VSSetup
-```
-
-Initialize Visual Studio in PowerShell with the following commands. We may setup
-this for all PowerShell sessions by writing it to the PowerShell profile. Change
-the library path according to your own case.
-
-```powershell
-Import-VisualStudioVars -Architecture x64
-$env:LIB += ";C:\Program Files\Python37\libs"
-```
-
-### Apple Silicon (M1/M2 Chips) ###
-
-We need PyTorch >= 1.13 to run TorchDrug on Apple silicon. For `torch-scatter` and
-`torch-cluster`, they can be compiled from their sources. Note TorchDrug doesn't
-support `mps` devices.
-
-```bash
-pip install torch==1.13.0
-pip install git+https://github.com/rusty1s/pytorch_scatter.git
-pip install git+https://github.com/rusty1s/pytorch_cluster.git
-pip install torchdrug
-```
-
-Quick Start
------------
-
-TorchDrug is designed for humans and focused on graph structured data.
-It enables easy implementation of graph operations in machine learning models.
-All the operations in TorchDrug are backed by [PyTorch] framework, and support GPU
-acceleration and auto differentiation.
-
-```python
-from torchdrug import data
-
-edge_list = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]]
-graph = data.Graph(edge_list, num_node=6)
-graph = graph.cuda()
-# the subgraph induced by nodes 2, 3 & 4
-subgraph = graph.subgraph([2, 3, 4])
-```
-
-Molecules are also supported in TorchDrug. You can get the desired molecule
-properties without any domain knowledge.
-
-```python
-mol = data.Molecule.from_smiles("CCOC(=O)N", atom_feature="default", bond_feature="default")
-print(mol.node_feature)
-print(mol.atom_type)
-print(mol.to_scaffold())
-```
-
-You may also register custom node, edge or graph attributes. They will be
-automatically processed during indexing operations.
-
-```python
-with mol.edge():
- mol.is_CC_bond = (mol.edge_list[:, :2] == td.CARBON).all(dim=-1)
-sub_mol = mol.subgraph(mol.atom_type != td.NITROGEN)
-print(sub_mol.is_CC_bond)
-```
-
-TorchDrug provides a wide range of common datasets and building blocks for drug
-discovery. With minimal code, you can apply standard models to solve your own
-problem.
-
-```python
-import torch
-from torchdrug import datasets
-
-dataset = datasets.Tox21()
-dataset[0].visualize()
-lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]
-lengths += [len(dataset) - sum(lengths)]
-train_set, valid_set, test_set = torch.utils.data.random_split(dataset, lengths)
-```
-
-```python
-from torchdrug import models, tasks
-
-model = models.GIN(dataset.node_feature_dim, hidden_dims=[256, 256, 256, 256])
-task = tasks.PropertyPrediction(model, task=dataset.tasks)
-```
-
-Training and inference are accelerated by multiple CPUs or GPUs.
-This can be seamlessly switched in TorchDrug by just a line of code.
-```python
-from torchdrug import core
-
-# Single CPU / Multiple CPUs / Distributed CPUs
-solver = core.Engine(task, train_set, valid_set, test_set, optimizer)
-# Single GPU
-solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0])
-# Multiple GPUs
-solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3])
-# Distributed GPUs
-solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3, 0, 1, 2, 3])
-```
-
-Experiments can be easily tracked and managed through [Weights & Biases platform].
-```python
-solver = core.Engine(task, train_set, valid_set, test_set, optimizer, logger="wandb")
-```
-
-[Weights & Biases platform]: https://wandb.ai/
-
-Contributing
-------------
-
-Everyone is welcome to contribute to the development of TorchDrug.
-Please refer to [contributing guidelines](CONTRIBUTING.md) for more details.
-
-License
--------
-
-TorchDrug is released under [Apache-2.0 License](LICENSE).
+
+[](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true)
+[](https://github.com/DeepGraphLearning/torchdrug/blob/master/CONTRIBUTING.md)
+[](https://github.com/DeepGraphLearning/torchdrug/blob/master/LICENSE)
+[](https://pypi.org/project/torchdrug/)
+[](https://twitter.com/DrugTorch)
+
+[Docs] | [Tutorials] | [Benchmarks] | [Papers Implemented]
+
+[Docs]: https://deepgraphlearning.github.io/torchdrug-site/docs
+[Tutorials]: https://deepgraphlearning.github.io/torchdrug-site/docs/tutorials
+[Benchmarks]: https://deepgraphlearning.github.io/torchdrug-site/docs/benchmark
+[Papers Implemented]: https://deepgraphlearning.github.io/torchdrug-site/docs/paper
+
+TorchDrug is a [PyTorch]-based machine learning toolbox designed for several purposes.
+
+- Easy implementation of graph operations in a PyTorchic style with GPU support
+- Being friendly to practitioners with minimal knowledge about drug discovery
+- Rapid prototyping of machine learning research
+
+[PyTorch]: https://pytorch.org/
+
+Installation
+------------
+
+TorchDrug can be installed on either Linux, Windows or macOS. It is compatible with
+3.7 <= Python <= 3.10 and PyTorch >= 1.8.0.
+
+### From Conda ###
+
+```bash
+conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg
+```
+
+### From Pip ###
+
+```bash
+pip install torch==1.9.0
+pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html
+pip install torchdrug
+```
+
+To install `torch-scatter` for other PyTorch or CUDA versions, please see the
+instructions in https://github.com/rusty1s/pytorch_scatter
+
+### From Source ###
+
+```bash
+git clone https://github.com/DeepGraphLearning/torchdrug
+cd torchdrug
+pip install -r requirements.txt
+python setup.py install
+```
+
+### Windows (PowerShell) ###
+
+We need to first install the build tools for Visual Studio. We then install the
+following modules in PowerShell.
+
+```powershell
+Install-Module Pscx -AllowClobber
+Install-Module VSSetup
+```
+
+Initialize Visual Studio in PowerShell with the following commands. We may setup
+this for all PowerShell sessions by writing it to the PowerShell profile. Change
+the library path according to your own case.
+
+```powershell
+Import-VisualStudioVars -Architecture x64
+$env:LIB += ";C:\Program Files\Python37\libs"
+```
+
+### Apple Silicon (M1/M2 Chips) ###
+
+We need PyTorch >= 1.13 to run TorchDrug on Apple silicon. For `torch-scatter` and
+`torch-cluster`, they can be compiled from their sources. Note TorchDrug doesn't
+support `mps` devices.
+
+```bash
+pip install torch==1.13.0
+pip install git+https://github.com/rusty1s/pytorch_scatter.git
+pip install git+https://github.com/rusty1s/pytorch_cluster.git
+pip install torchdrug
+```
+
+Quick Start
+-----------
+
+TorchDrug is designed for humans and focused on graph structured data.
+It enables easy implementation of graph operations in machine learning models.
+All the operations in TorchDrug are backed by [PyTorch] framework, and support GPU
+acceleration and auto differentiation.
+
+```python
+from torchdrug import data
+
+edge_list = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]]
+graph = data.Graph(edge_list, num_node=6)
+graph = graph.cuda()
+# the subgraph induced by nodes 2, 3 & 4
+subgraph = graph.subgraph([2, 3, 4])
+```
+
+Molecules are also supported in TorchDrug. You can get the desired molecule
+properties without any domain knowledge.
+
+```python
+mol = data.Molecule.from_smiles("CCOC(=O)N", atom_feature="default", bond_feature="default")
+print(mol.node_feature)
+print(mol.atom_type)
+print(mol.to_scaffold())
+```
+
+You may also register custom node, edge or graph attributes. They will be
+automatically processed during indexing operations.
+
+```python
+with mol.edge():
+ mol.is_CC_bond = (mol.edge_list[:, :2] == td.CARBON).all(dim=-1)
+sub_mol = mol.subgraph(mol.atom_type != td.NITROGEN)
+print(sub_mol.is_CC_bond)
+```
+
+TorchDrug provides a wide range of common datasets and building blocks for drug
+discovery. With minimal code, you can apply standard models to solve your own
+problem.
+
+```python
+import torch
+from torchdrug import datasets
+
+dataset = datasets.Tox21()
+dataset[0].visualize()
+lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]
+lengths += [len(dataset) - sum(lengths)]
+train_set, valid_set, test_set = torch.utils.data.random_split(dataset, lengths)
+```
+
+```python
+from torchdrug import models, tasks
+
+model = models.GIN(dataset.node_feature_dim, hidden_dims=[256, 256, 256, 256])
+task = tasks.PropertyPrediction(model, task=dataset.tasks)
+```
+
+Training and inference are accelerated by multiple CPUs or GPUs.
+This can be seamlessly switched in TorchDrug by just a line of code.
+```python
+from torchdrug import core
+
+# Single CPU / Multiple CPUs / Distributed CPUs
+solver = core.Engine(task, train_set, valid_set, test_set, optimizer)
+# Single GPU
+solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0])
+# Multiple GPUs
+solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3])
+# Distributed GPUs
+solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3, 0, 1, 2, 3])
+```
+
+Experiments can be easily tracked and managed through [Weights & Biases platform].
+```python
+solver = core.Engine(task, train_set, valid_set, test_set, optimizer, logger="wandb")
+```
+
+[Weights & Biases platform]: https://wandb.ai/
+
+Contributing
+------------
+
+Everyone is welcome to contribute to the development of TorchDrug.
+Please refer to [contributing guidelines](CONTRIBUTING.md) for more details.
+
+License
+-------
+
+TorchDrug is released under [Apache-2.0 License](LICENSE).
Datasets
Models
