Implemention of paper : Magicmol - A light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration
A copy of this work is available at https://zenodo.org/record/7776906#.ZCsbYcpBwUE
Magicmol provides a light-weighted pipeline for de novo novel molecule generation and evolution (combine with STONED-SELFIES), and can be utilized for either positive sampling or negative sampling. The process can be visulized in the following picture.
Original ChEMBL30 (https://www.ebi.ac.uk/chembl/) dataset, cleaned dataset are available at : https://drive.google.com/drive/folders/1ULI0ZxBk26EiCw_p0LnLB1ckgstWK0Hq?usp=sharing
190W: Original Chembl-30 small molecule dataset
database.cleaned.smi / database.smi : pure SMILES data / SMILES data cleaned by clean.py
database_smiles_0.25.pkl / database_smiles_0.5.pkl : random choosed 25% / 50% of the cleaned molecule data
python clean.py --in_path database.smi --out_path database_cleaned.smi --vocab_path ../vocab/chembl_selfies_vocab.yaml
The weight file is available at : https://drive.google.com/drive/folders/10LDOoQQzuL0XeDRdsWidhVsoN8W1wxFU?usp=sharing
Update: Model trained with SMILES is provided also in the above link.
Update: SMILES / DeepSMILES corpus and processing code are available now (see vocab). Temporarily our Model adopting DeepSMILES reaches a relatively lower performance (with approximately 70% validity for generating molecules, I may tried to fix it later).
However, we recommend you train the backbone model by yourself and leave it a cup of tea time (approximately 15 min on a single RTX3090) to finish this (XD) and experience how fast the process could be finished. The hardware for training and sampling can be altered as long as CUDA is available, and 6G video memory will also be affluent for training or sampling.
By default the training process is done by adopting SELFIES, more super parameters can be customized based on your requirements.
python train.py
For example, training with SMILES:
python train.py --num_embeddings 101 --vocab_path ./vocab/chembl_regex_vocab.yaml --which_vocab regex
In our work, optimization is done by varying the SA score judged by SYBA from sampled molecules derived from the well-trained backbone model.
For positive optimization:
python synthetic_accessibility_modification.py --model_weight_path ../model_parameters/trained_model.pth --use_syba True --optim_task posi
For negative optimization:
python synthetic_accessibility_modification.py --model_weight_path ../model_parameters/trained_model.pth --use_syba True --optim_task nega
Sampling with SELFIES corpus, for SMILES or DeepSMILES, change them to correspond to your local directory
python sample.py --num_samples 1024 --num_batches 100 --vocab_path vocab/chembl_selfies_vocab.yaml --model_weight_path model_parameters/trained_model.pth --which_vocab selfies
We provided a simple way for computing some metrics. See after_generate.py.
STONED-SELFIES is available at: https://github.com/aspuru-guzik-group/stoned-selfies
SYBA is available at https://github.com/lich-uct/syba
If you think our research is interesting, consider citing this paper? 👇
Chen, L., Shen, Q., & Lou, J. (2023). Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration. BMC bioinformatics, 24(1), 173. https://doi.org/10.1186/s12859-023-05286-0
Noted that our work was done by SELIFES 1.x, the above version may caused encoding problems.
Pickle needs python 3.8 or higher version for using pickle.HIGHEST_PROTOCOL.