3DMolCSP

(free for academic use)

Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations

Yuhui Hong, Christopher J. Welch, Patrick Piras, and Haixu Tang*

This work introduces 3D molecular representation learning to enhance the prediction of enantioselectivity of Chiral Stationary Phases (CSPs) and elution order of enantiomers in the field of chromatography, which can assist in the selection of CSPs for practical applications. Our [paper] published on Analytical Chemistry provides further details.

Set up

# RDKit 2022.03.5
# https://www.rdkit.org/docs/GettingStartedInPython.html
conda create -c rdkit -n <env-name> rdkit
conda activate <env-name>
# please use rdkit >= 2021.03.4
# https://github.com/rdkit/rdkit/pull/4272

# Pytorch 1.13.0
# Please choose the proper cuda version from their official website:
# https://pytorch.org/get-started/previous-versions/
conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c conda-forge tensorboard

pip install lxml tqdm pandas pyteomics PyYAML scikit-learn

Data preprocessing

Demo set (Chirobiotic V)

# The demo dataset is already cleaned. 
# generate 3D conformations
python ./preprocess/gen_conformers.py --path ./data/demo/chirobiotic_v.sdf --conf_type etkdg

ChirBase

# preprocessing
python ./preprocess/preprocess_chirbase.py \
--input ./data/ChirBase/chirbase.sdf \
--output ./data/ChirBase/chirbase_clean.sdf \
--csp_setting ./preprocess/chirality_stationary_phase_list.csv

# generate 3D conformations
python ./preprocess/gen_conformers.py --path ./data/ChirBase/chirbase_clean.sdf --conf_type etkdg

# (optional) OMEGA conformations are available
# OMEGA algorithm requires a license. Please get the license here: https://www.eyesopen.com/omega
# python ./preprocess/gen_conformers.py --path ./data/ChirBase/chirbase_clean.sdf --conf_type omega --license <path to OMEGA license>

# (optional) randomly split training and validation set for Exp3
python ./preprocess/random_split_sdf.py \
--input ./data/ChirBase/chirbase_clean_etkdg.sdf \
--output_train ./data/ChirBase/chirbase_clean_etkdg_train.sdf \
--output_test ./data/ChirBase/chirbase_clean_etkdg_test.sdf
# python ./preprocess/random_split_sdf.py \
# --input ./data/ChirBase/chirbase_clean_omega.sdf \
# --output_train ./data/ChirBase/chirbase_clean_omega_train.sdf \
# --output_test ./data/ChirBase/chirbase_clean_omega_test.sdf

CMRT

# preprocessing
python ./preprocess/preprocess_cmrt.py \
--input ./data/CMRT/cmrt_all_column.csv \
--output ./data/CMRT/cmrt_clean.sdf \
--csp_setting ./preprocess/chirality_stationary_phase_list.csv

# generate 3D conformations
python ./preprocess/gen_conformers.py --path ./data/CMRT/cmrt_clean.sdf --conf_type etkdg

Experiments

Exp1: Demo

1. Preprocess demo dataset

2. Five-fold cross-validation

# training from scratch
python main_chir_kfold.py --config ./configs/molnet_train_demo.yaml --k_fold 5 --csp_no 3 \
                                    --log_dir ./logs/molnet_chirality/ \
                                    --checkpoint ./check_point/demo_sc.pt \
                                    --result_path ./demo/demo_sc.csv \
                                    --device 1

# training from pretrained model 
python main_chir_kfold.py --config ./configs/molnet_train_demo.yaml --k_fold 5 --csp_no 3 \
                                    --log_dir ./logs/molnet_chirality/ \
                                    --resume_path ./check_point/molnetv2_qtof_etkdgv3.pt \
                                    --transfer \
                                    --checkpoint ./check_point/demo_tl.pt \
                                    --result_path ./demo/demo_tl.csv \
                                    --device 1

Results on demo dataset:

# --------------- Final Results of 3DMolCSP-SC --------------- #
fold_0: acc: 0.81, auc: 0.95
fold_1: acc: 0.90, auc: 0.99
fold_2: acc: 0.73, auc: 0.87
fold_3: acc: 0.81, auc: 0.96
fold_4: acc: 0.84, auc: 0.98
mean acc: 0.82, mean auc: 0.95

# --------------- Final Results of 3DMolCSP-TL --------------- #
fold_0: acc: 0.80, auc: 0.94
fold_1: acc: 0.91, auc: 0.99
fold_2: acc: 0.81, auc: 0.97
fold_3: acc: 0.79, auc: 0.96
fold_4: acc: 0.80, auc: 0.97
mean acc: 0.82, mean auc: 0.97

Exp2: Five-fold cross-validation on ChirBase

1. Preprocess ChirBase

2. Five-fold cross-validation

# training from scratch
nohup bash ./experiments/train_chir_etkdg_5fold.sh > molnet_chir_etkdg_5fold.out 

# traning from pre-trained model
nohup bash ./experiments/train_chir_etkdg_5fold_tl.sh > molnet_chir_etkdg_5fold_tl.out 

Exp3: Training on ChirBase and testing on CMRT

1. Training (using all data)

# traning from pre-trained model
nohup bash ./experiments/train_chir_etkdg_tl.sh > molnet_chir_etkdg_tl.out 

2. Preprocess CMRT

3. infer on CMRT

nohup bash ./experiments/infer_cmrt_etkdg_tl.sh > molnet_cmrt_etkdg_tl_infer.out 

Exp4: Elution order prediction

nohup bash ./experiments/train_chir_etkdg_eo.sh > train_chir_etkdg_eo.out 

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