[DGL-LifeSci] Documentation (dmlc#1414)

* Update

* Update

* Update

apps/life_sci/README.md

@@ -1,5 +1,7 @@
 # DGL-LifeSci
 
+[Documentation](https://lifesci.dgl.ai/index.html) | [Discussion Forum](https://discuss.dgl.ai)
+
 ## Introduction
 
 Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in 

apps/life_sci/docs/source/api/data.rst

@@ -1,47 +1,61 @@
 .. _apidata:
 
-dgllife.data
-============
+Datasets
+========
 
-TBD by Murphy
+.. contents:: Contents
+    :local:
 
-dgllife.data.alchemy
---------------------
+Molecular Property Prediction
+-----------------------------
 
-.. automodule:: dgllife.data.alchemy
-    :members:
+Tox21
+`````
 
+.. autoclass:: dgllife.data.Tox21
+    :members: task_pos_weights, __getitem__, __len__
+    :show-inheritance:
 
-dgllife.data.csv_dataset
-------------------------
+Alchemy for Quantum Chemistry
+`````````````````````````````
 
-.. automodule:: dgllife.data.csv_dataset
-    :members:
+.. autoclass:: dgllife.data.TencentAlchemyDataset
+    :members: set_mean_and_std, __getitem__, __len__
 
+Pubmed Aromaticity
+``````````````````
 
-dgllife.data.pdbbind
----------------------
+.. autoclass:: dgllife.data.PubChemBioAssayAromaticity
+    :members: __getitem__, __len__
+    :show-inheritance:
 
-.. automodule:: dgllife.data.pdbbind
-    :members:
+Adapting to New Datasets with CSV
+`````````````````````````````````
 
+.. autoclass:: dgllife.data.MoleculeCSVDataset
+    :members: __getitem__, __len__
 
-dgllife.data.pubchem_aromaticity
----------------------------------
+Reaction Prediction
+-------------------
 
-.. automodule:: dgllife.data.pubchem_aromaticity
-    :members:
+USPTO
+`````
 
+.. autoclass:: dgllife.data.USPTO
+    :members: __getitem__, __len__
+    :show-inheritance:
 
-dgllife.data.tox21
----------------------------------
+Adapting to New Datasets for Weisfeiler-Lehman Networks
+```````````````````````````````````````````````````````
 
-.. automodule:: dgllife.data.tox21
-    :members:
+.. autoclass:: dgllife.data.WLNReactionDataset
+    :members: __getitem__, __len__
 
+Protein-Ligand Binding Affinity Prediction
+------------------------------------------
 
-dgllife.data.uspto
----------------------------------
+PDBBind
+```````
 
-.. automodule:: dgllife.data.uspto
-    :members:
+.. autoclass:: dgllife.data.PDBBind
+    :members: __getitem__, __len__

apps/life_sci/docs/source/api/model.gnn.rst

@@ -1,42 +1,45 @@
 .. _apimodelgnn:
 
-dgllife.model.gnn
-==================
+Graph Neural Networks for Updating Node/Edge Representations
+============================================================
 
-TBD by Murphy
+All models based on graph neural networks start with updating node/edge representations.
+We introduce various GNN models implemented in DGL-LifeSci for representation update.
 
-dgllife.model.gnn.attentivefp
--------------------------------------------
+.. contents:: Contents
+    :local:
+
+AttentiveFP
+-----------
 .. automodule:: dgllife.model.gnn.attentivefp
     :members:
 
-
-dgllife.model.gnn.gat
-----------------------------
+GAT
+---
 .. automodule:: dgllife.model.gnn.gat
     :members:
 
-dgllife.model.gnn.gcn
-----------------------------
+GCN
+---
 .. automodule:: dgllife.model.gnn.gcn
     :members:
 
-dgllife.model.gnn.mgcn
-----------------------------
+MGCN
+----
 .. automodule:: dgllife.model.gnn.mgcn
     :members:
 
-dgllife.model.gnn.mpnn
-----------------------------
+MPNN
+----
 .. automodule:: dgllife.model.gnn.mpnn
     :members:
 
-dgllife.model.gnn.schnet
-----------------------------
+SchNet
+------
 .. automodule:: dgllife.model.gnn.schnet
     :members:
 
-dgllife.model.gnn.wln
-----------------------------
+WLN
+---
 .. automodule:: dgllife.model.gnn.wln
     :members:

apps/life_sci/docs/source/api/model.pretrain.rst

@@ -0,0 +1,66 @@
+.. _apimodelpretrain:
+
+Pre-trained Models
+==================
+
+We provide multiple pre-trained models for users to use without the need of training from scratch.
+
+Example Usage
+-------------
+
+Property Prediction
+```````````````````
+
+.. code-block:: python
+
+    from dgllife.data import Tox21
+    from dgllife.model import load_pretrained
+    from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
+
+    dataset = Tox21(smiles_to_bigraph, CanonicalAtomFeaturizer())
+    model = load_pretrained('GCN_Tox21') # Pretrained model loaded
+    model.eval()
+
+    smiles, g, label, mask = dataset[0]
+    feats = g.ndata.pop('h')
+    label_pred = model(g, feats)
+    print(smiles)                   # CCOc1ccc2nc(S(N)(=O)=O)sc2c1
+    print(label_pred[:, mask != 0]) # Mask non-existing labels
+    # tensor([[ 1.4190, -0.1820,  1.2974,  1.4416,  0.6914,
+    # 2.0957,  0.5919,  0.7715, 1.7273,  0.2070]])
+
+Generative Models
+
+.. code-block:: python
+
+    from dgllife.model import load_pretrained
+
+    model = load_pretrained('DGMG_ZINC_canonical')
+    model.eval()
+    smiles = []
+    for i in range(4):
+        smiles.append(model(rdkit_mol=True))
+
+    print(smiles)
+    # ['CC1CCC2C(CCC3C2C(NC2=CC(Cl)=CC=C2N)S3(=O)=O)O1',
+    # 'O=C1SC2N=CN=C(NC(SC3=CC=CC=N3)C1=CC=CO)C=2C1=CCCC1',
+    # 'CC1C=CC(=CC=1)C(=O)NN=C(C)C1=CC=CC2=CC=CC=C21',
+    # 'CCN(CC1=CC=CC=C1F)CC1CCCN(C)C1']
+
+If you are running the code block above in Jupyter notebook, you can also visualize the molecules generated with
+
+.. code-block:: python
+
+    from IPython.display import SVG
+    from rdkit import Chem
+    from rdkit.Chem import Draw
+
+    mols = [Chem.MolFromSmiles(s) for s in smiles]
+    SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
+
+.. image:: https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png
+
+API
+---
+
+.. autofunction:: dgllife.model.load_pretrained

apps/life_sci/docs/source/api/model.readout.rst

@@ -1,25 +1,28 @@
 .. _apimodelreadout:
 
-dgllife.model.readout
-========================
+Readout for Computing Graph Representations
+===========================================
 
-TBD by Murphy
+After updating node/edge representations with graph neural networks (GNNs), a common operation is to compute
+graph representations out of updated node/edge representations. For example, we need to compute molecular
+representations out of atom/bond representations in molecular property prediction. We call the various modules
+for computing graph-level representations **readout** as in Neural Message Passing for Quantum Chemistry and this
+section lists the readout modules implemented in DGL-LifeSci.
 
-dgllife.model.readout.attentivefp_readout
-------------------------------------------
+.. contents:: Contents
+    :local:
+
+AttentiveFP Readout
+-------------------
 .. automodule:: dgllife.model.readout.attentivefp_readout
     :members:
 
-
-
-dgllife.model.readout.mlp_readout
-------------------------------------------
+MLP Readout
+-----------
 .. automodule:: dgllife.model.readout.mlp_readout
     :members:
 
-
-
-dgllife.model.readout.weighted_sum_and_max
---------------------------------------------
+Weighted Sum and Max Readout
+----------------------------
 .. automodule:: dgllife.model.readout.weighted_sum_and_max
     :members:

apps/life_sci/docs/source/api/model.zoo.rst

@@ -1,61 +1,77 @@
 .. _apimodelzoo:
 
-dgllife.model.model_zoo
-=============================
+Model Zoo
+=========
 
-TBD by Murphy
+This section introduces complete models for various downstream tasks.
 
-dgllife.model.model_zoo.attentivefp_predictor
------------------------------------------------
+.. contents:: Contents
+    :local:
+
+Building Blocks
+---------------
+
+MLP Predictor
+`````````````
+.. automodule:: dgllife.model.model_zoo.mlp_predictor
+    :members:
+
+Molecular Property Prediction
+-----------------------------
+
+AttentiveFP Predictor
+`````````````````````
 .. automodule:: dgllife.model.model_zoo.attentivefp_predictor
     :members:
 
-dgllife.model.model_zoo.gat_predictor
--------------------------------------------
+GAT Predictor
+`````````````
 .. automodule:: dgllife.model.model_zoo.gat_predictor
     :members:
 
-dgllife.model.model_zoo.gcn_predictor
--------------------------------------------
+GCN Predictor
+`````````````
 .. automodule:: dgllife.model.model_zoo.gcn_predictor
     :members:
 
-dgllife.model.model_zoo.mgcn_predictor
--------------------------------------------
+MGCN Predictor
+``````````````
 .. automodule:: dgllife.model.model_zoo.mgcn_predictor
     :members:
 
-dgllife.model.model_zoo.mlp_predictor
--------------------------------------------
-.. automodule:: dgllife.model.model_zoo.mlp_predictor
-    :members:
-
-dgllife.model.model_zoo.mpnn_predictor
--------------------------------------------
+MPNN Predictor
+``````````````
 .. automodule:: dgllife.model.model_zoo.mpnn_predictor
     :members:
 
-dgllife.model.model_zoo.schnet_predictor
--------------------------------------------
+SchNet Predictor
+````````````````
 .. automodule:: dgllife.model.model_zoo.schnet_predictor
     :members:
 
-dgllife.model.model_zoo.wln_reaction_center
--------------------------------------------
-.. automodule:: dgllife.model.model_zoo.wln_reaction_center
-    :members:
+Generative Models
+-----------------
 
-dgllife.model.model_zoo.acnn
--------------------------------------------
-.. automodule:: dgllife.model.model_zoo.acnn
-    :members:
-
-dgllife.model.model_zoo.dgmg
--------------------------------------------
+DGMG
+````
 .. automodule:: dgllife.model.model_zoo.dgmg
     :members:
 
-dgllife.model.model_zoo.jtnn
--------------------------------------------
+JTNN
+````
 .. autoclass:: dgllife.model.model_zoo.jtnn.DGLJTNNVAE
     :members:
+
+Reaction Prediction
+
+WLN for Reaction Center Prediction
+``````````````````````````````````
+.. automodule:: dgllife.model.model_zoo.wln_reaction_center
+    :members:
+
+Protein-Ligand Binding Affinity Prediction
+
+ACNN
+````
+.. automodule:: dgllife.model.model_zoo.acnn
+    :members: