v0.2.0

README.md

@@ -6,9 +6,10 @@
   </a>
 </h1>
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true)
 [![Contributions](https://img.shields.io/badge/contributions-welcome-blue)](https://github.com/DeepGraphLearning/torchdrug/blob/master/CONTRIBUTING.md)
 [![License Apache-2.0](https://img.shields.io/github/license/DeepGraphLearning/torchdrug?color=blue)](https://github.com/DeepGraphLearning/torchdrug/blob/master/LICENSE)
+[![PyPI downloads](https://static.pepy.tech/personalized-badge/torchdrug?period=total&units=international_system&left_color=grey&right_color=blue&left_text=downloads)](https://pypi.org/project/torchdrug/)
 [![TorchDrug Twitter](https://img.shields.io/twitter/url?label=TorchDrug&style=social&url=https%3A%2F%2Ftwitter.com%2FDrugTorch)](https://twitter.com/DrugTorch)
 
 [Docs] | [Tutorials] | [Benchmarks] | [Papers Implemented]
@@ -42,7 +43,7 @@ conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg
 
 ```bash
 pip3 install torch==1.9.0
-pip3 install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html
+pip3 install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html
 pip3 install torchdrug
 ```
 

conda/torchdrug/meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: torchdrug
-  version: 0.1.3
+  version: 0.2.0
 
 source:
   path: ../..
@@ -13,6 +13,7 @@ requirements:
     - python >=3.7,<3.10
     - pytorch >=1.8.0
     - pytorch-scatter >=2.0.8
+    - pytorch-cluster >=1.5.9
     - decorator
     - numpy >=1.11
     - rdkit >=2020.09
@@ -21,6 +22,8 @@ requirements:
     - networkx
     - ninja
     - jinja2
+    - python-lmdb
+    - bio-embeddings-esm
 
 build:
   noarch: python

doc/source/installation.rst

@@ -30,7 +30,7 @@ and CUDA is ``10.2``, the command should be
 
 .. code:: bash
 
-    pip3 install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
+    pip3 install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
 
 Replace the versions in the above url according to your case. If you don't have GPUs
 or CUDA installed, treat the CUDA version in the url as ``cpu``. See

docker/Dockerfile

@@ -1,7 +1,7 @@
 FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-devel
 
 # metainformation
-LABEL org.opencontainers.image.version = "0.1.3"
+LABEL org.opencontainers.image.version = "0.2.0"
 LABEL org.opencontainers.image.authors = "TorchDrug Team"
 LABEL org.opencontainers.image.source = "https://github.com/DeepGraphLearning/torchdrug"
 LABEL org.opencontainers.image.licenses = "Apache License 2.0"
@@ -11,5 +11,5 @@ RUN apt-get update && \
     apt-get install -y libxrender1 && \
     rm -rf /var/lib/apt/lists/*
 
-RUN pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.1+cu111.html  && \
+RUN pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.1+cu111.html && \
     pip install torchdrug

requirements.txt

@@ -1,10 +1,13 @@
 torch>=1.8.0
 torch-scatter>=2.0.8
+torch-cluster>=1.5.9
 decorator
 numpy>=1.11
-rdkit-pypi
+rdkit-pypi>=2020.9
 matplotlib
 tqdm
 networkx
 ninja
-jinja2
+jinja2
+lmdb
+fair-esm

setup.py

@@ -13,7 +13,7 @@
         long_description_content_type="text/markdown",
         url="https://torchdrug.ai/",
         author="TorchDrug Team",
-        version="0.1.3",
+        version="0.2.0",
         license="Apache-2.0",
         keywords=["deep-learning", "pytorch", "drug-discovery"],
         packages=setuptools.find_packages(),
@@ -31,6 +31,7 @@
         install_requires=[
             "torch>=1.8.0",
             "torch-scatter>=2.0.8",
+            "torch-cluster>=1.5.9",
             "decorator",
             "numpy>=1.11",
             "rdkit-pypi>=2020.9",
@@ -39,6 +40,8 @@
             "networkx",
             "ninja",
             "jinja2",
+            "lmdb",
+            "fair-esm",
         ],
         python_requires=">=3.7,<3.10",
         classifiers=[

torchdrug/__init__.py

@@ -12,4 +12,4 @@
 handler.setFormatter(format)
 logger.addHandler(handler)
 
-__version__ = "0.1.3"
+__version__ = "0.2.0"

torchdrug/layers/common.py

@@ -74,7 +74,7 @@ def forward(self, input):
 class GaussianSmearing(nn.Module):
     r"""
     Gaussian smearing from
-    `SchNet: A continuous-filter convolutional neural network for modeling quantum interactions`_.``
+    `SchNet: A continuous-filter convolutional neural network for modeling quantum interactions`_.
 
     There are two modes for Gaussian smearing.