Release and docs (#85)

Author: benedekrozemberczki

README.md

@@ -26,6 +26,20 @@
 
 --------------------------------------------------------------------------------
 
+**Citing**
+
+
+If you find *ChemicalX* and the new datasets useful in your research, please consider adding the following citation:
+
+```bibtex
+@inproceedings{chemicalx,
+               author = {Benedek Rozemberczki and Charles Tapley Hoyt and Anna Gogleva and Piotr Grabowski and Klas Karis and Andrej Lamov and Andriy Nikolov and Sebastian Nilsson and Michael Ughetto and Yu Wang and Tyler Derr and Benjamin Gyori},
+               title = {{ChemicalX: A Deep Learning Library fo Drug Pair Scoring}},
+               year = {2022},
+}
+```
+--------------------------------------------------------------------------------
+
 **Drug Pair Scoring Explained**
 
 Our framework solves the [drug pair scoring task](https://arxiv.org/abs/2111.02916) of computational chemistry. In this task a machine learning model has to predict the outcome of administering two drugs together in a biological or chemical context. Deep learning models which solve this task have an architecture with two distinctive parts:
@@ -80,19 +94,6 @@ We provide in-depth case study like tutorials in the [Documentation](https://ch
 
 --------------------------------------------------------------------------------
 
-**Citing**
-
-
-If you find *ChemicalX* and the new datasets useful in your research, please consider adding the following citation:
-
-```bibtex
-@inproceedings{chemicalx,
-               author = {Benedek Rozemberczki and Charles Tapley Hoyt and Benjamin Gyori},
-               title = {{ChemicalX: A Deep Learning Library fo Drug Pair Scoring}},
-               year = {2022},
-}
-```
-
 **Methods Included**
 
 In detail, the following drug pair scoring models were implemented.

chemicalx/models/deepsynergy.py

@@ -12,7 +12,7 @@
 
 
 class DeepSynergy(Model):
-    r"""The DeepSynergy model from [preuer2018]_.
+    r"""An implementation of the DeepSynergy model from [preuer2018]_.
 
     .. seealso:: This model was suggested in https://github.com/AstraZeneca/chemicalx/issues/16
 

chemicalx/models/epgcnds.py

@@ -16,7 +16,7 @@
 
 
 class EPGCNDS(Model):
-    r"""The EPGCN-DS model from [sun2020]_.
+    r"""An implementation of the EPGCN-DS model from [sun2020]_.
 
     .. seealso:: This model was suggested in https://github.com/AstraZeneca/chemicalx/issues/22
 

chemicalx/version.py

@@ -1,3 +1,3 @@
 """Contains the version of ChemicalX."""
 
-__version__ = "0.0.9"
+__version__ = "0.1.0"

setup.py

@@ -54,13 +54,13 @@
 setup(
     name="chemicalx",
     packages=find_packages(),
-    version="0.0.8",
+    version="0.1.0",
     license="Apache License, Version 2.0",
     description="A Deep Learning Library for Drug Pair Scoring.",
     author="Benedek Rozemberczki and Charles Hoyt",
     author_email="[email protected]",
     url="https://github.com/AstraZeneca/chemicalx",
-    download_url="https://github.com/AstraZeneca/chemicalx/archive/v0.0.8.tar.gz",
+    download_url="https://github.com/AstraZeneca/chemicalx/archive/v0.1.0.tar.gz",
     keywords=keywords,
     install_requires=install_requires,
     setup_requires=setup_requires,