marcossilva · marcossilva · Aug 11, 2022 · Feb 24, 2022 · Mar 10, 2022 · Mar 10, 2022
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
@@ -21,3 +21,4 @@ Contributors
 * [Wenxuan Fan](https://github.com/wenx00): Strategies for Pre-training Graph Neural Networks
 * [Vignesh Venkataraman](https://github.com/VIGNESHinZONE): PAGTN
 * [Eric O. Korman](https://github.com/ekorman): Fix for ogbg_ppa
+* [Marcos Leal](https://github.com/marcossilva): Change default number of processes to 1 for rexgen
diff --git a/README.md b/README.md
@@ -4,20 +4,30 @@
 
 We also have a **slack channel** for real-time discussion. If you want to join the channel, contact mufeili1996@gmail.com.
 
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Installation](#installation)
+  * [Requirements](#requirements)
+  * [Pip installation for DGL-LifeSci](#pip-installation-for-dgl-lifesci)
+  * [Installation from source](#installation-from-source)
+  * [Verifying successful installation](#verifying-successful-installation)
+- [Command Line Interface](#command-line-interface)
+- [Examples](#examples)
+- [Cite](#cite)
+
 ## Introduction
 
-Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in 
-life science such as molecular graphs and biological networks, making it an import area for applying 
-deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science 
-with graph neural networks. 
+Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in
+life science such as molecular graphs and biological networks, making it an import area for applying
+deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science
+with graph neural networks.
 
-We provide various functionalities, including but not limited to methods for graph construction, 
+We provide various functionalities, including but not limited to methods for graph construction,
 featurization, and evaluation, model architectures, training scripts and pre-trained models.
 
 For a list of community contributors, see [here](CONTRIBUTORS.md).
 
-**For a full list of work implemented in DGL-LifeSci, see [here](examples/README.md).**
-
 ## Installation
 
 ### Requirements
@@ -46,7 +56,7 @@ Additionally, we require `RDKit 2018.09.3` for utils related to cheminformatics.
 ```
 conda install -c rdkit rdkit==2018.09.3
 ```
- 
+
 For other installation recipes for RDKit, see the [official documentation](https://www.rdkit.org/docs/Install.html).
 
 ### Pip installation for DGL-LifeSci
@@ -67,7 +77,7 @@ python setup.py install
 
 ### Verifying successful installation
 
-Once you have installed the package, you can verify the success of installation with 
+Once you have installed the package, you can verify the success of installation with
 
 ```python
 import dgllife
@@ -76,7 +86,18 @@ print(dgllife.__version__)
 # 0.2.9
 ```
 
-### Cite
+## Command Line Interface
+
+DGL-LifeSci provides command line interfaces that allow users to perform modeling without any background in programming and deep learning. You will need to first clone the github repo.
+
+- [Molecular Property Prediction](examples/property_prediction/csv_data_configuration/)
+- [Reaction Prediction](examples/reaction_prediction/rexgen_direct/)
+
+## Examples
+
+For a full list of work implemented in DGL-LifeSci, see [here](examples/README.md).
+
+## Cite
 
 If you use DGL-LifeSci in a scientific publication, we would appreciate citations to the following paper:
 

diff --git a/docs/source/api/model.gnn.rst b/docs/source/api/model.gnn.rst
@@ -63,3 +63,13 @@ GNNOGB
 ------
 .. automodule:: dgllife.model.gnn.gnn_ogb
     :members:
+
+NF
+--
+.. automodule:: dgllife.model.gnn.nf
+    :members:
+
+PAGTN
+-----
+.. automodule:: dgllife.model.gnn.pagtn
+    :members:
diff --git a/docs/source/api/model.zoo.rst b/docs/source/api/model.zoo.rst
@@ -69,6 +69,16 @@ GNN OGB Predictor
 .. automodule:: dgllife.model.model_zoo.gnn_ogb_predictor
     :members:
 
+Neural Fingerprint Predictor
+````````````````````````````
+.. automodule:: dgllife.model.model_zoo.nf_predictor
+    :members:
+
+Path-Augmented Graph Transformer Predictor
+``````````````````````````````````````````
+.. automodule:: dgllife.model.model_zoo.pagtn_predictor
+    :members:
+
 Generative Models
 -----------------
 
@@ -77,6 +87,11 @@ DGMG
 .. automodule:: dgllife.model.model_zoo.dgmg
     :members:
 
+JTNNVAE
+```````
+.. automodule:: dgllife.model.model_zoo.jtvae
+    :members:
+
 Reaction Prediction
 
 WLN for Reaction Center Prediction
@@ -95,3 +110,8 @@ ACNN
 ````
 .. automodule:: dgllife.model.model_zoo.acnn
     :members:
+
+PotentialNet
+````````````
+.. automodule:: dgllife.model.model_zoo.potentialnet
+    :members:
diff --git a/docs/source/api/utils.complexes.rst b/docs/source/api/utils.complexes.rst
@@ -9,3 +9,4 @@ Utilities in DGL-LifeSci for working with protein-ligand complexes.
     :toctree: ../generated/
 
     dgllife.utils.ACNN_graph_construction_and_featurization
+    dgllife.utils.PN_graph_construction_and_featurization
diff --git a/docs/source/api/utils.mols.rst b/docs/source/api/utils.mols.rst
@@ -49,6 +49,9 @@ three common graph constructions:
     dgllife.utils.k_nearest_neighbors
     dgllife.utils.mol_to_nearest_neighbor_graph
     dgllife.utils.smiles_to_nearest_neighbor_graph
+    dgllife.utils.ToGraph
+    dgllife.utils.MolToBigraph
+    dgllife.utils.SMILESToBigraph
 
 Featurization for Molecules
 ---------------------------
@@ -130,6 +133,8 @@ For using featurization methods like above in creating node features:
     dgllife.utils.PretrainAtomFeaturizer
     dgllife.utils.AttentiveFPAtomFeaturizer
     dgllife.utils.AttentiveFPAtomFeaturizer.feat_size
+    dgllife.utils.PAGTNAtomFeaturizer
+    dgllife.utils.PAGTNAtomFeaturizer.feat_size
 
 Featurization for Edges
 ```````````````````````
@@ -164,3 +169,5 @@ For using featurization methods like above in creating edge features:
     dgllife.utils.PretrainBondFeaturizer
     dgllife.utils.AttentiveFPBondFeaturizer
     dgllife.utils.AttentiveFPBondFeaturizer.feat_size
+    dgllife.utils.PAGTNEdgeFeaturizer
+    dgllife.utils.PAGTNEdgeFeaturizer.feat_size
diff --git a/docs/source/api/utils.pipeline.rst b/docs/source/api/utils.pipeline.rst
@@ -21,4 +21,4 @@ Early stopping is a standard practice for preventing models from overfitting and
 class for handling it.
 
 .. autoclass:: dgllife.utils.EarlyStopping
-    :members:
+    :members: step
diff --git a/docs/source/cli.rst b/docs/source/cli.rst
@@ -0,0 +1,35 @@
+.. _cli:
+
+Command Line Interface
+======================
+
+DGL-LifeSci provides command line interfaces that allow users
+to perform modeling without any background in programming and
+deep learning. In addition to installation, you will need to
+clone the github repo with
+
+.. code:: bash
+
+    git clone https://github.com/awslabs/dgl-lifesci.git
+
+Molecular Property Prediction
+-----------------------------
+
+Go to the directory below with
+
+.. code:: bash
+
+    cd dgl-lifesci/examples/property_prediction/csv_data_configuration/
+
+and then follow the README file.
+
+Reaction Prediction
+-------------------
+
+Go to the directory below with
+
+.. code:: bash
+
+    cd dgl-lifesci/examples/reaction_prediction/rexgen_direct/
+
+and then follow the README file.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -2,25 +2,12 @@ DGL-LifeSci: Bringing Graph Neural Networks to Chemistry and Biology
 ===========================================================================================
 
 DGL-LifeSci is a python package for applying graph neural networks to various tasks in chemistry
-and biology, on top of PyTorch and DGL. It provides:
+and biology, on top of PyTorch, DGL, and RDKit. It covers various applications, including:
 
-* Various utilities for data processing, training and evaluation.
-* Efficient and flexible model implementations.
-* Pre-trained models for use without training from scratch.
-
-We cover various applications in our
-`examples <https://github.com/awslabs/dgl-lifesci/tree/master/examples>`_, including:
-
-* `Molecular property prediction <https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction>`_
-* `Attention visualization <https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/pubchem_aromaticity>`_
-* `Generative models <https://github.com/awslabs/dgl-lifesci/tree/master/examples/generative_models>`_
-* `Protein-ligand binding affinity prediction <https://github.com/awslabs/dgl-lifesci/tree/master/examples/binding_affinity_prediction>`_
-* `Reaction prediction <https://github.com/awslabs/dgl-lifesci/tree/master/examples/reaction_prediction>`_
-
-Get Started
-------------
-
-Follow the :doc:`instructions<install/index>` to install DGL.
+* Molecular property prediction
+* Generative models
+* Reaction prediction
+* Protein-ligand binding affinity prediction
 
 .. toctree::
    :maxdepth: 1
@@ -29,6 +16,7 @@ Follow the :doc:`instructions<install/index>` to install DGL.
    :glob:
 
    install/index
+   cli
 
 .. toctree::
     :maxdepth: 2
@@ -50,7 +38,3 @@ Free software
 -------------
 DGL-LifeSci is free software; you can redistribute it and/or modify it under the terms
 of the Apache License 2.0. We welcome contributions. Join us on `GitHub <https://github.com/awslabs/dgl-lifesci>`_.
-
-Index
------
-* :ref:`genindex`
diff --git a/docs/source/install/index.rst b/docs/source/install/index.rst
@@ -1,11 +1,9 @@
-Install DGL-LifeSci
-===================
-
-This topic explains how to install DGL-LifeSci. We recommend installing DGL-LifeSci by using ``conda`` or ``pip``.
+Installation
+============
 
 System requirements
 -------------------
-DGL-LifeSci works with the following operating systems:
+DGL-LifeSci should work on:
 
 * Ubuntu 16.04
 * macOS X
@@ -17,15 +15,6 @@ DGL-LifeSci requires:
 * `DGL 0.4.3 or later <https://www.dgl.ai/pages/start.html>`_
 * `PyTorch 1.2.0 or later <https://pytorch.org/>`_
 
-If you have just installed DGL, the first time you use it, a message will pop up as follows:
-
-.. code:: bash
-
-    DGL does not detect a valid backend option. Which backend would you like to work with?
-    Backend choice (pytorch, mxnet or tensorflow):
-
-and you need to enter ``pytorch``.
-
 Additionally, we require **RDKit 2018.09.3** for cheminformatics. We recommend installing it with
 
 .. code:: bash
@@ -34,15 +23,6 @@ Additionally, we require **RDKit 2018.09.3** for cheminformatics. We recommend i
 
 Other verions of RDKit are not tested.
 
-Install from conda
-----------------------
-If ``conda`` is not yet installed, get either `miniconda <https://conda.io/miniconda.html>`_ or
-the full `anaconda <https://www.anaconda.com/download/>`_.
-
-.. code:: bash
-
-    conda install -c dglteam dgllife
-
 Install from pip
 ----------------
 

diff --git a/examples/README.md b/examples/README.md
@@ -66,6 +66,9 @@ We provide various examples across 3 applications -- property prediction, genera
 - 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](../python/dgllife/model/model_zoo/acnn.py)
     - [Example Training Script](binding_affinity_prediction)
+- PotentialNet for molecular property prediction (PotentialNet) [[paper]](https://pubs.acs.org/doi/10.1021/acscentsci.8b00507)
+    - [PotentialNet with DGL](../python/dgllife/model/model_zoo/potentialnet.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)

diff --git a/examples/property_prediction/MTL/README.md b/examples/property_prediction/MTL/README.md
@@ -24,9 +24,10 @@ For demonstration, you can generate a synthetic dataset as follows.
 import torch
 import pandas as pd
 
+# 'nan' for missing property labels
 data = {
-    'smiles': ['CCO' for _ in range(128)],
-    'logP': torch.randn(128).numpy().tolist(),
+    'smiles': ['CCO', 'CO', 'C', 'O'] * 32,
+    'logP': torch.randn(127).numpy().tolist() + [float('nan')],
     'logD': torch.randn(128).numpy().tolist()
 }
 df = pd.DataFrame(data)

diff --git a/examples/property_prediction/MTL/main.py b/examples/property_prediction/MTL/main.py
@@ -31,7 +31,7 @@
 
     from argparse import ArgumentParser
     from dgllife.data import MoleculeCSVDataset
-    from dgllife.utils import smiles_to_bigraph, RandomSplitter
+    from dgllife.utils import SMILESToBigraph, RandomSplitter
 
     from configure import configs
     from run import main
@@ -67,13 +67,14 @@
     # Setup for experiments
     mkdir_p(args['result_path'])
 
-    node_featurizer = atom_featurizer
     edge_featurizer = CanonicalBondFeaturizer(bond_data_field='he', self_loop=True)
     df = pd.read_csv(args['csv_path'])
+
+    smiles_to_g = SMILESToBigraph(add_self_loop=True, node_featurizer=atom_featurizer,
+                                  edge_featurizer=edge_featurizer)
+
     dataset = MoleculeCSVDataset(
-        df, partial(smiles_to_bigraph, add_self_loop=True),
-        node_featurizer=node_featurizer,
-        edge_featurizer=edge_featurizer,
+        df, smiles_to_g,
         smiles_column=args['smiles_column'],
         cache_file_path=args['result_path'] + '/graph.bin',
         task_names=args['tasks']
@@ -84,4 +85,4 @@
         dataset, frac_train=0.8, frac_val=0.1,
         frac_test=0.1, random_state=0)
 
-    main(args, node_featurizer, edge_featurizer, train_set, val_set, test_set)
+    main(args, atom_featurizer, edge_featurizer, train_set, val_set, test_set)
diff --git a/examples/property_prediction/csv_data_configuration/classification_inference.py b/examples/property_prediction/csv_data_configuration/classification_inference.py
@@ -9,17 +9,17 @@
 import torch
 
 from dgllife.data import UnlabeledSMILES
-from dgllife.utils import mol_to_bigraph
-from functools import partial
+from dgllife.utils import MolToBigraph
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from utils import mkdir_p, collate_molgraphs_unlabeled, load_model, predict, init_featurizer
 
 def main(args):
-    dataset = UnlabeledSMILES(args['smiles'], node_featurizer=args['node_featurizer'],
-                              edge_featurizer=args['edge_featurizer'],
-                              mol_to_graph=partial(mol_to_bigraph, add_self_loop=True))
+    mol_to_g = MolToBigraph(add_self_loop=True,
+                            node_featurizer=args['node_featurizer'],
+                            edge_featurizer=args['edge_featurizer'])
+    dataset = UnlabeledSMILES(args['smiles'], mol_to_graph=mol_to_g)
     dataloader = DataLoader(dataset, batch_size=args['batch_size'],
                             collate_fn=collate_molgraphs_unlabeled, num_workers=args['num_workers'])
     model = load_model(args).to(args['device'])