q20110911/dtnn_7ib

This is a deep learning model used to predict molecular energy

DTNN_7ib

Molecular energy and ligand stability prediction models based on deep neural tensor networks and MMFF optimized geometries.

Project Organization

├── LICENSE
├── Makefile           <- Makefile, used to create new environment, install requiremnts.
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data used to generate results in paper.
│   ├── processed      
│   └── raw            <- The original, immutable data dump.
├── models             <- Trained models.
├── notebooks          <- Jupyter notebooks inlcuding tutorials.
├── reports            <- Generated analysis results.
│   └── result_data    <- Result data for confs, used to get conformation stability result.
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── prepare_dataset.py
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Detailed Information

Setup:

Setup functions are in Makefile. To see functions in Makefile:

make -f Makefile

1. Create DTNN_7ib environment:

make Makefile create_environment

2. Activate environment:

source activate DTNN_7ib 

3. Install requirements:

make Makefile requirements

Data Preparation: (src/data)

All scipts for data prepartion are in src/data directory. To see the option for data prepation:

python prepare_dataset.py --help

To build qm9mmff dataset for training, validation, testlive and testing

python prepare_dataset.py

To build eMol9_C_M:

python prepare_dataset.py --datatype emol9mmff

To build Plati_C_M:

python prepare_dataset.py --datatype platinummmff

raw data are in data/raw directory, processed data are in data/processed directory. The processed data we used in data/external directory

Model Training: (src/model)

All scipts for model training are in src/data directory. To see the option for model training:

python train_model.py --help

Train DTNN_7ib model:

python train_model.py --addnewm 

Train TL_QM9_M:

python train_model.py --geometry MMFF --transferlearning

Train TL_eMol9_C_M:

python train_model.py --datatype emol9mmff --geometry MMFF --transferlearning

Prediction:(src/model)

All scipts for prediction are in src/data directory. To see the option for prediction:

python predict_model.py --help

Performanc of DTNN_7ib on QM9:

python predict_model.py 

Performanc of DTNN_7ib on QM9MMFF:

python predict_model.py  --testpositions mmffpositions

Performance of TL_QM9_M on QM9MMFF:

python predict_model.py --modelname TL_QM9_name --testpositions mmffpositions

Peformance of TL_eMol9_C_M on eMol9_C_M:

python predict_model.py --modelname TL_eMOL9_CM_name --testtype emol9mmff --testpositions positions1

Peformance of TL_eMol9_C_M on Plati_C_M:

python predict_model.py --modelname TL_eMOL9_CM_name --testtype platinummmff --testpositions positions1

Note:

1. If you directly run predict_model.py, the MAE and RMSE is for DTNN_7ib trained with best validation error in one of splits (performance of DTNN_7ib in paper is the avarage of five different splits) and transfer learning is applied on the atom vectors learned from this DTNN_7ib model
   
2. remember to change the model name
   

Thanks for DTNN code(https://github.com/atomistic-machine-learning/dtnn), we reimplemented elementary blocks in DTNN.
Ref:Jianing Lu, Cheng Wang and Yingkai Zhang. J. Chem. Theory Comput. https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.9b00001