💊 MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization

This repository hosts MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization (AAAI) 2021 (Tianfan Fu, Cao Xiao, Xinhao Li, Lucas Glass, Jimeng Sun), which used pretrained graph neural network (GNN) and MCMC for molecule optimization.

Table Of Contents

• Installation
• Data
• Pretraining
• Run
• Contact

⚙️ 1. Installation

To install locally, we recommend to install from pip and conda. Please see conda.yml for the package dependency.

conda create -n mimosa python=3.7 
conda activate mimosa
pip install torch 
pip install PyTDC 
conda install -c rdkit rdkit 

Activate conda environment.

conda activate mimosa

make directory

mkdir -p save_model result 

📊 2. Data

In our setup, we restrict the number of oracle calls. In realistic discovery settings, the oracle acquisition cost is usually not negligible.

Raw Data

We use ZINC database, which contains around 250K drug-like molecules and can be downloaded download ZINC.

python src/download.py

• output
  • data/zinc.tab: all the smiles in ZINC, around 250K.

Oracle

Oracle is a property evaluator and is a function whose input is molecular structure, and output is the property. We consider following oracles:

• JNK3: biological activity to JNK3, ranging from 0 to 1.
• GSK3B biological activity to GSK3B, ranging from 0 to 1.
• QED: Quantitative Estimate of Drug-likeness, ranging from 0 to 1.
• SA: Synthetic Accessibility, we normalize SA to (0,1).
• LogP: solubility and synthetic accessibility of a compound. It ranges from negative infinity to positive infinity.

For all the property scores above, higher is more desirable.

Optimization Task

There are two kinds of optimization tasks: single-objective and multi-objective optimization. Multi-objective optimization contains jnkgsk (JNK3 + GSK3B), qedsajnkgsk (QED + SA + JNK3 + GSK3B).

Generate Vocabulary

In this project, the basic unit is substructure, which can be atoms or single rings. The vocabulary is the set of frequent substructures.

python src/vocabulary.py

• input
  • data/zinc.tab: all the smiles in ZINC, around 250K.
• output
  • data/substructure.txt: including all the substructures in ZINC.
  • data/vocabulary.txt: vocabulary, frequent substructures.

data cleaning

We remove the molecules that contains substructure that is not in vocabulary.

python src/clean.py 

• input
  • data/vocabulary.txt: vocabulary
  • data/zinc.tab: all the smiles in ZINC
• output
  • data/zinc_clean.txt

Pre-train graph neural network (GNN)

python src/train.py 

• input
  • data/zinc_clean.txt
• output
  • save_model/GNN.ckpt: trained GNN model.
• log
  • gnn_loss.pkl: the valid loss.

🤖 Run

de novo molecule design

python src/run.py

• input
  • save_model/GNN.ckpt: pretrained GNN model.
• output
  • result/{$prop}.pkl: set of generated molecules.

For example,

python src/run.py

evaluate

python src/evaluate.py $prop 

• input
  • result/{$prop}.pkl
• output
  • diversity, novelty, average property of top-100 molecules with highest property.

For example,

python src/evaluate.py jnkgsk 

📞 Contact

Please contact futianfan@gmail.com for help or submit an issue.

Cite Us

If you found this package useful, please cite our paper:

@inproceedings{fu2021mimosa,
  title={MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization},
  author={Fu, Tianfan and Xiao, Cao and Li, Xinhao and Glass, Lucas M and Sun, Jimeng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={1},
  pages={125--133},
  year={2021}
}