Update (#1366)

apps/life_sci/README.md

@@ -21,9 +21,15 @@ Depending on the features you want to use, you may need to manually install the
 - RDKit 2018.09.3
     - We recommend installation with `conda install -c conda-forge rdkit==2018.09.3`. For other installation recipes,
     see the [official documentation](https://www.rdkit.org/docs/Install.html).
-- (optional) MDTraj
-    - We recommend installation with `conda install -c conda-forge mdtraj`. For alternative ways of installation, 
-    see the [official documentation](http://mdtraj.org/1.9.3/installation.html).
+    
+## Installation
+
+To install the package, 
+
+```bash
+cd python
+python setup.py install
+```
 
 ## Organization
 

apps/life_sci/examples/README.md

@@ -5,49 +5,49 @@ We provide various examples across 3 applications -- property prediction, genera
 ## Datasets/Benchmarks
 
 - MoleculeNet: A Benchmark for Molecular Machine Learning [[paper]](https://arxiv.org/abs/1703.00564), [[website]](http://moleculenet.ai/)
-    - [Tox21 with DGL](../dgllife/data/tox21.py)
-    - [PDBBind with DGL](../dgllife/data/pdbbind.py)
+    - [Tox21 with DGL](../python/dgllife/data/tox21.py)
+    - [PDBBind with DGL](../python/dgllife/data/pdbbind.py)
 - Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models [[paper]](https://arxiv.org/abs/1906.09427), [[github]](https://github.com/tencent-alchemy/Alchemy)
-    - [Alchemy with DGL](../dgllife/data/alchemy.py)
+    - [Alchemy with DGL](../python/dgllife/data/alchemy.py)
 
 ## Property Prediction
 
 - Semi-Supervised Classification with Graph Convolutional Networks (GCN) [[paper]](https://arxiv.org/abs/1609.02907), [[github]](https://github.com/tkipf/gcn)
-    - [GCN-Based Predictor with DGL](../dgllife/model/model_zoo/gcn_predictor.py)
+    - [GCN-Based Predictor with DGL](../python/dgllife/model/model_zoo/gcn_predictor.py)
     - [Example for Molecule Classification](property_prediction/classification.py)
 - Graph Attention Networks (GAT) [[paper]](https://arxiv.org/abs/1710.10903), [[github]](https://github.com/PetarV-/GAT)
-    - [GAT-Based Predictor with DGL](../dgllife/model/model_zoo/gat_predictor.py)
+    - [GAT-Based Predictor with DGL](../python/dgllife/model/model_zoo/gat_predictor.py)
     - [Example for Molecule Classification](property_prediction/classification.py)
 - SchNet: A continuous-filter convolutional neural network for modeling quantum interactions [[paper]](https://arxiv.org/abs/1706.08566), [[github]](https://github.com/atomistic-machine-learning/SchNet)
-    - [SchNet with DGL](../dgllife/model/model_zoo/schnet_predictor.py)
+    - [SchNet with DGL](../python/dgllife/model/model_zoo/schnet_predictor.py)
     - [Example for Molecule Regression](property_prediction/regression.py)
 - Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective (MGCN) [[paper]](https://arxiv.org/abs/1906.11081)
-    - [MGCN with DGL](../dgllife/model/model_zoo/mgcn_predictor.py)
+    - [MGCN with DGL](../python/dgllife/model/model_zoo/mgcn_predictor.py)
     - [Example for Molecule Regression](property_prediction/regression.py)
 - Neural Message Passing for Quantum Chemistry (MPNN) [[paper]](https://arxiv.org/abs/1704.01212), [[github]](https://github.com/brain-research/mpnn)
-    - [MPNN with DGL](../dgllife/model/model_zoo/mpnn_predictor.py)
+    - [MPNN with DGL](../python/dgllife/model/model_zoo/mpnn_predictor.py)
     - [Example for Molecule Regression](property_prediction/regression.py)
 - Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism (AttentiveFP) [[paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.9b00959)
-    - [AttentiveFP with DGL](../dgllife/model/model_zoo/attentivefp_predictor.py)
+    - [AttentiveFP with DGL](../python/dgllife/model/model_zoo/attentivefp_predictor.py)
     - [Example for Molecule Regression](property_prediction/regression.py)
 
 ## Generative Models
 
 - Learning Deep Generative Models of Graphs (DGMG) [[paper]](https://arxiv.org/abs/1803.03324)
-    - [DGMG with DGL](../dgllife/model/model_zoo/dgmg.py)
+    - [DGMG with DGL](../python/dgllife/model/model_zoo/dgmg.py)
     - [Example Training Script](generative_models/dgmg)
 - Junction Tree Variational Autoencoder for Molecular Graph Generation (JTNN) [[paper]](https://arxiv.org/abs/1802.04364)
-    - [JTNN with DGL](../dgllife/model/model_zoo/jtnn)
+    - [JTNN with DGL](../python/dgllife/model/model_zoo/jtnn)
     - [Example Training Script](generative_models/jtnn)
 
 ## Binding Affinity Prediction
 
 - Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity (ACNN) [[paper]](https://arxiv.org/abs/1703.10603), [[github]](https://github.com/deepchem/deepchem/tree/master/contrib/atomicconv)
-    - [ACNN with DGL](../dgllife/model/model_zoo/acnn.py)
+    - [ACNN with DGL](../python/dgllife/model/model_zoo/acnn.py)
     - [Example Training Script](binding_affinity_prediction)
 
 ## Reaction Prediction
 - A graph-convolutional neural network model for the prediction of chemical reactivity [[paper]](https://pubs.rsc.org/en/content/articlelanding/2019/sc/c8sc04228d#!divAbstract), [[github]](https://github.com/connorcoley/rexgen_direct)
     - An earlier version was published in NeurIPS 2017 as "Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network" [[paper]](https://arxiv.org/abs/1709.04555)
-    - [WLN with DGL for Reaction Center Prediction](../dgllife/model/model_zoo/wln_reaction_center.py)
+    - [WLN with DGL for Reaction Center Prediction](../python/dgllife/model/model_zoo/wln_reaction_center.py)
     - [Example Script](reaction_prediction/rexgen_direct)