fragmentation and vectorization of small molecules
git clone https://github.com/OnlyBelter/molFrag2vec.git- download miniconda, please see https://conda.io/en/master/miniconda.html
- also see: Building identical conda environments
cd /where/is/molFrag2vec/located
conda create --name molFrag2vec --file requirements.txtconda activate molFrag2vec
# install rdkit and mol2vec
conda install -c rdkit rdkit==2019.03.3.0
pip install git+https://github.com/samoturk/mol2vec
# install HDBSCAN and mordred
conda install -c conda-forge hdbscan
pip install git+https://github.com/mordred-descriptor/mordred
# install tensorflow
pip install tensorflow==2.1.0$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
#$ git checkout tags/v0.9.1
$ pip install .
$ cd ..
$ rm -rf fastText- The first step: generate molecular tree based on https://github.com/wengong-jin/icml18-jtnn. Then we can get the fragments of each molecule and the relation between each two fragments.
python step1_mol_tree.py demo_data/demo_dataset.txt demo_data/step1_result.txt --log_fn demo_data/step1_log.log
# or calculate molecular tree of examples in paper
python step1_mol_tree.py dataset/examples_in_paper/cid2smiles.txt dataset/examples_in_paper/step1_result.txt --log_fn dataset/examples_in_paper/step1_log.loga. generate fragment sentence
b. count fragment
c. replace fragment SMILES by fragment id (optional)
python step2_refragment.py ./demo_data/step1_result.txt ./demo_data/ --log_fn ./demo_data/step2_log.log
# plot molecular structure, molecular tree and molecular with index of the first 10 lines under test model
python step2_refragment.py ./demo_data/step1_result.txt ./demo_data/ --log_fn ./demo_data/step2_log.log --test- step2_cid2frag_id_sentence.csv: cid and fragment id sentence
- step2_cid2smiles_sentence.csv: cid and SMILES sentence
- step2_frag2num.csv: count the number of fragments in all dataset
- step2_frag_id_sentence.csv: only fragment id sentence separated by space, can be used to training model
Training molFrag2vec model by FastText, and get the vectors of all fragments.
python step3_training_frag_vec_model.py ./demo_data/step2_frag_id_sentence.csv ./demo_data/step3_molFrag2vec_demo.bin- step3_frag2vec_model_molFrag2vec.csv: fragment vectors
- step3_molFrag2vec_demo.bin: well-trained unsupervised frag2vec model
Calculate molecular vector of selected molecules which have been classed by classyFire
python step4_smiles2vec.pyCluster selected molecules by DBSCAN algorithm, and calculate purity score based on classyFire superclass
python clustering.py './demo_data' './demo_data' --include_small_dataset_dir './dataset' --model molFrag2vec- s2_trained_model_molFrag2vec3.bin: https://doi.org/10.6084/m9.figshare.11589477
- s2_trained_model_kmeans_model.pkl: https://doi.org/10.6084/m9.figshare.11589477
- mol2vec.csv: https://doi.org/10.6084/m9.figshare.11589870
- pure_kmeans_class.csv: https://doi.org/10.6084/m9.figshare.11599773
and save in directory: molFrag2vec/big-data/...
# only get molecular vectors
python class_prediction.py big-data/all_cid2smiles/x_training_set_cid2_sentence_new.csv big-data/frag2vec_model_fragTandem2vec_new.csv --result_dir big-data/all_cid2smiles/ --log_fn big-data/all_cid2smiles/class_prediction_log.log
# calculate nearest neighbors
# need mol2vec.csv file, add by --training_mol_vec_fp parameter
python class_prediction.py dataset/examples_in_paper/step2_cid2frag_id_sentence.csv big-data/frag2vec_model_fragTandem2vec_new.csv --result_dir dataset/examples_in_paper/ --training_mol_vec_fp big-data/all_cid2smiles/mol_vec_all_training_set_model_molFrag2vec.csv --log_fn dataset/examples_in_paper/class_prediction.log --find_nearest_neighbors
# predict class
# python class_prediction.py ./big-data/s2_trained_model_molFrag2vec3.bin ./demo_data/step2_result.txt --clustering_model_fp ./big-data/s2_trained_model_kmeans_model.pkl --result_dir ./demo_data --log_fn ./demo_data/step3_log.log --pure_kmeans_class_fp ./big-data/pure_kmeans_class.csv --predict_class附件1：所有碎片以及出现的次数，dataset/fragTandem2vec_related/frag2num_training_set_model_fragTandem2vec.csv 附件2：训练好的碎片向量模型
- Mol2vec model: dataset/mol2vec_related/mol2vec_model.pkl
- fragTandem2vec model: