- Thank PARATERA company (https://www.paratera.com/) for the cooperations
- database :
- DrugBank : DrugBank all-in-one sdf file, and the scripts for generating the training and the testing suits
- polyfitted : Fitted ploynormal equations for selected 89 reference DFT functionals
- rawdata : The assembled Gaussuian09-D.01 timing data, and the separated sdf files with added H atoms
- trained-models : Trained models for few DFT functional/basis set combinations
- example : The sample molecule to be predicted
- src : source code folder
- tools : Independent scripts
- experimental : Some scripts in developing or experimental stage
- TRmod_kernel_A1.py : Training script sample
- Fcst_kernel_A1.py : Predicting script sample
- Prerequisities
- python3 with numpy, scipy, scikit-learn
- pytorch, with CUDA, cudatoolkit, torchvision, dgl, gensim
- basis_set_exchange, libxc
- rdkit, openbabel
- optional: xlsxwriter, pillow
- Installation example (recommended with conda):
- git clone git@github.com:yingjin-ma/Fcst_sys_public.git Fcst_sys_public
- cd Fcst_sys_public
- conda create -n Fcst_sys_public python=3.8 (3.7 is also tested)
- conda activate Fcst_sys_public
- conda install rdkit pytorch gensim torchvision numpy scipy xlsxwriter scikit-learn basis_set_exchange libxc matplotlib tqdm
- Suggested installing order:
- conda install rdkit
- conda install pytorch=1.11.0=cuda112py38habe9d5a_1
- conda install gensim torchvision numpy scipy xlsxwriter scikit-learn basis_set_exchange libxc matplotlib tqdm
- install dgl cuda version (notice at least the major version should match that of installed cudatoolkit)
- conda install -c dglteam dgl-cuda11.3
- now: python TRmod_kernel_A1.py should work
- Install the pylibxc (Please see https://www.tddft.org/programs/libxc/installation/)
- now: python Fcst_kernel_A1.py should work
- python TRmod_kernel_A1.py for training
- python Fcst_kernel_A1.py for predicting
- python Fcst_kernel_A1_LB_wrapper.py for load-balancing
- The "Predicted_Loads.txt" will be generated for later usage
- More will be added
- National Key Research and Development Program of China (Grant No.2018YFB0203805)
- National Natural Science Foundation of China (Grant No.21703260)
- Informationization Program of the Chinese Academy of Science (Grant No.XXH13506-403)
- Guangdong Provincial Key Laboratory of Biocomputing (Grant No.2016B030301007)