MolRep: A Deep Representation Learning Library for Molecular Property Prediction
Summary
MolRep is a Python package for fairly measuring algorithmic progress on chemical property datasets. It currently provides a complete re-evaluation of 16 state-of-the-art deep representation models over 16 benchmark property datsaets.
If you found this package useful, please cite biorxiv for now:
Install & Usage
We provide a script to install the environment. You will need the conda package manager, which can be installed from here.
To install the required packages, follow there instructions (tested on a linux terminal):
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clone the repository
git clone https://github.com/Jh-SYSU/MolRep
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cdinto the cloned directorycd MolRep
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run the install script
source install.sh [<your_cuda_version>]
Where <your_cuda_version> is an optional argument that can be either cpu, cu92, cu100, cu101. If you do not provide a cuda version, the script will default to cpu. The script will create a virtual environment named MolRep, with all the required packages needed to run our code. Important: do NOT run this command using bash instead of source!
Data
Data could be download from Google_Driver
Current Dataset
| Dataset | Task | Task type | #Molecule | Splits | Metric | Reference |
|---|---|---|---|---|---|---|
| QM7 | 1 | Regression | 7160 | Stratified | MAE | Wu et al. |
| QM8 | 12 | Regression | 21786 | Random | MAE | Wu et al. |
| QM9 | 12 | Regression | 133885 | Random | MAE | Wu et al. |
| ESOL | 1 | Regression | 1128 | Random | RMSE | Wu et al. |
| FreeSolv | 1 | Regression | 642 | Random | RMSE | Wu et al. |
| Lipophilicity | 1 | Regression | 4200 | Random | RMSE | Wu et al. |
| BBBP | 1 | Classification | 2039 | Scaffold | ROC-AUC | Wu et al. |
| Tox21 | 12 | Classification | 7831 | Random | ROC-AUC | Wu et al. |
| SIDER | 27 | Classification | 1427 | Random | ROC-AUC | Wu et al. |
| ClinTox | 2 | Classification | 1478 | Random | ROC-AUC | Wu et al. |
| Liver injury | 1 | Classification | 2788 | Random | ROC-AUC | Xu et al. |
| Mutagenesis | 1 | Classification | 6511 | Random | ROC-AUC | Hansen et al. |
| hERG | 1 | Classification | 4813 | Random | ROC-AUC | Li et al. |
| MUV | 17 | Classification | 93087 | Random | PRC-AUC | Wu et al. |
| HIV | 1 | Classification | 41127 | Random | ROC-AUC | Wu et al. |
| BACE | 1 | Classification | 1513 | Random | ROC-AUC | Wu et al. |
Methods
Current Methods
Self-/unsupervised Models
| Methods | Descriptions | Reference |
|---|---|---|
| Mol2Vec | Mol2Vec is an unsupervised approach to learns vector representations of molecular substructures that point in similar directions for chemically related substructures. | Jaeger et al. |
| N-Gram graph | N-gram graph is a simple unsupervised representation for molecules that first embeds the vertices in the molecule graph and then constructs a compact representation for the graph by assembling the ver-tex embeddings in short walks in the graph. | Liu et al. |
| FP2Vec | FP2Vec is a molecular featurizer that represents a chemical compound as a set of trainable embedding vectors and combine with CNN model. | Jeon et al. |
| VAE | VAE is a framework for training two neural networks (encoder and decoder) to learn a mapping from high-dimensional molecular representation into a lower-dimensional space. | Kingma et al. |
Sequence Models
| Methods | Descriptions | Reference |
|---|---|---|
| BiLSTM | BiLSTM is an artificial recurrent neural network (RNN) architecture to encoding sequences from compound SMILES strings. | Hochreiter et al. |
| SALSTM | SALSTM is a self-attention mechanism with improved BiLSTM for molecule representation. | Zheng et al |
| Transformer | Transformer is a network based solely on attention mechanisms and dispensing with recurrence and convolutions entirely to encodes compound SMILES strings. | Vaswani et al. |
| MAT | MAT is a molecule attention transformer utilized inter-atomic distances and the molecular graph structure to augment the attention mechanism. | Maziarka et al. |
Graph Models
| Methods | Descriptions | Reference |
|---|---|---|
| DGCNN | DGCNN is a deep graph convolutional neural network that proposes a graph convolution model with SortPooling layer which sorts graph vertices in a consistent order to learning the embedding of molec-ular graph. | Zhang et al. |
| GraphSAGE | GraphSAGE is a framework for inductive representation learning on molecular graphs that used to generate low-dimensional representations for atoms and performs sum, mean or max-pooling neigh-borhood aggregation to updates the atom representation and molecular representation. | Hamilton et al. |
| GIN | GIN is the Graph Isomorphism Network that builds upon the limitations of GraphSAGE to capture different graph structures with the Weisfeiler-Lehman graph isomorphism test. | Xu et al. |
| ECC | ECC is an Edge-Conditioned Convolution Network that learns a different parameter for each edge label (bond type) on the molecular graph, and neighbor aggregation is weighted according to specific edge parameters. | Simonovsky et al. |
| DiffPool | DiffPool combines a differentiable graph encoder with its an adaptive pooling mechanism that col-lapses nodes on the basis of a supervised criterion to learning the representation of molecular graphs. | Ying et al. |
| MPNN | MPNN is a message-passing graph neural network that learns the representation of compound molecular graph. It mainly focused on obtaining effective vertices (atoms) embedding | Gilmer et al. |
| D-MPNN | DMPNN is another message-passing graph neural network that messages associated with directed edges (bonds) rather than those with vertices. It can make use of the bond attributes. | Yang et al. |
| CMPNN | CMPNN is the graph neural network that improve the molecular graph embedding by strengthening the message interactions between edges (bonds) and nodes (atoms). | Song et al. |
Training
To train a model by K-fold, run 5-fold-training_example.ipynb.
Testing
To test a pretrained model, run testing-example.ipynb.
Results
Results on Classification Tasks.
| Datasets | BBBP | Tox21 | SIDER | ClinTox | MUV | HIV | BACE |
|---|---|---|---|---|---|---|---|
| Mol2Vec | 0.9213±0.0052 | 0.8139±0.0081 | 0.6043±0.0061 | 0.8572±0.0054 | 0.1178±0.0032 | 0.8413±0.0047 | 0.8284±0.0023 |
| N-Gram graph | 0.9012±0.0385 | 0.8371±0.0421 | 0.6482±0.0437 | 0.8753±0.0077 | 0.1011±0.0000 | 0.8378±0.0034 | 0.8472±0.0057 |
| FP2Vec | 0.8076±0.0032 | 0.8578±0.0076 | 0.6678±0.0068 | 0.8834±0.0432 | 0.0856±0.0031 | 0.7894±0.0052 | 0.8129±0.0492 |
| VAE | 0.8378±0.0031 | 0.8315±0.0382 | 0.6493±0.0762 | 0.8674±0.0124 | 0.0794±0.0001 | 0.8109±0.0381 | 0.8368±0.0762 |
| BiLSTM | 0.8391±0.0032 | 0.8279±0.0098 | 0.6092±0.0303 | 0.8319±0.0120 | 0.0382±0.0000 | 0.7962±0.0098 | 0.8263±0.0031 |
| SALSTM | 0.8482±0.0329 | 0.8253±0.0031 | 0.6308±0.0036 | 0.8317±0.0003 | 0.0409±0.0000 | 0.8034±0.0128 | 0.8348±0.0019 |
| Transformer | 0.9610±0.0119 | 0.8129±0.0013 | 0.6017±0.0012 | 0.8572±0.0032 | 0.0716±0.0017 | 0.8372±0.0314 | 0.8407±0.0738 |
| MAT | 0.9620±0.0392 | 0.8393±0.0039 | 0.6276±0.0029 | 0.8777±0.0149 | 0.0913±0.0001 | 0.8653±0.0054 | 0.8519±0.0504 |
| DGCNN | 0.9311±0.0434 | 0.7992±0.0057 | 0.6007±0.0053 | 0.8302±0.0126 | 0.0438±0.0000 | 0.8297±0.0038 | 0.8361±0.0034 |
| GraphSAGE | 0.9630±0.0474 | 0.8166±0.0041 | 0.6403±0.0045 | 0.9116±0.0146 | 0.1145±0.0000 | 0.8705±0.0724 | 0.9316±0.0360 |
| GIN | 0.8746±0.0359 | 0.8178±0.0031 | 0.5904±0.0000 | 0.8842±0.0004 | 0.0832±0.0000 | 0.8015±0.0328 | 0.8275±0.0034 |
| ECC | 0.9620±0.0003 | 0.8677±0.0090 | 0.6750±0.0092 | 0.8862±0.0831 | 0.1308±0.0013 | 0.8733±0.0025 | 0.8419±0.0092 |
| DiffPool | 0.8732±0.0391 | 0.8012±0.0130 | 0.6087±0.0130 | 0.8345±0.0233 | 0.0934±0.0001 | 0.8452±0.0042 | 0.8592±0.0391 |
| MPNN | 0.9321±0.0312 | 0.8440±0.014 | 0.6313±0.0121 | 0.8414±0.0294 | 0.0572±0.0001 | 0.8032±0.0092 | 0.8493±0.0013 |
| DMPNN | 0.9562±0.0070 | 0.8429±0.0391 | 0.6378±0.0329 | 0.8692±0.0051 | 0.0867±0.0032 | 0.8137±0.0072 | 0.8678±0.0372 |
| CMPNN | 0.9854±0.0215 | 0.8593±0.0088 | 0.6581±0.0020 | 0.9169±0.0065 | 0.1435±0.0002 | 0.8687±0.0003 | 0.8932±0.0019 |
More results will be updated soon.
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