drugd/rmcd

The code for 'Next-generation Molecular Graph Generation: Leveraging Regression Perception and Relationship-aware Diffusion'

Regressor-free Molecule Generation to support Drug Response Prediction (Regressor-free guidance)

Official Code Repository for the paper 'Regressor-free Molecule Generation to support Drug Response Prediction'.

Contribution

• Regressor-free guidance molecule generation is proposed to ensure sampling within a more effective space, where the regression controller model can encode the response value between the molecules and the cell lines as conditional guidance for molecule generation.
• To enhance noise prediction performance, we introduce a dual-branch controlled noise prediction model for score estimation, named DBControl. DBControl model consists of two GNN-based branches, each undergoing unconditional training and conditional mixed training, respectively.
• Experimental results demonstrate that our method outperforms the state-of-the-art baselines in conditional molecular graph generation for the DRP task. Furthermore, we provide an effectiveness proof of our method.

Dependencies

Regressor-free guidance is built in Python 3.8.18 and Pytorch 2.0.1. Please use the following command to install the requirements:

pip install -r requirements.txt

For molecule generation, additionally run the following command:

conda install -c conda-forge rdkit=2020.09.1.0

Running Experiments

1. Preparations

We provide two molecular graph datasets (QM9 and ZINC250k) and one drug response dataset (GDSCv2).

We additionally provide the commands for generating generic graph datasets as follows:

python data/data_generators.py --dataset ${dataset_name}

To preprocess the molecular graph datasets for training models, run the following command:

python data/preprocess.py --dataset ${dataset_name}
python data/preprocess_for_nspdk.py --dataset ${dataset_name}

For the evaluation of generic graph generation tasks, run the following command to compile the ORCA program (see http://www.biolab.si/supp/orca/orca.html):

cd evaluation/orca 
g++ -O2 -std=c++11 -o orca orca.cpp

2. Configurations

The configurations are provided on the config/ directory in YAML format. Hyperparameters used in the experiments are specified in the Appendix C of our paper.

3. Training Uncondition Model

We provide the commands for the following task: Molecule Generation.

To train the score models, first modify config/${dataset}.yaml accordingly, then run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type train --config ${train_config} --seed ${seed}

for example,

CUDA_VISIBLE_DEVICES=0 python main.py --type train --config community_small --seed 42

and

CUDA_VISIBLE_DEVICES=0,1 python main.py --type train --config zinc250k --seed 42

4. Training Condition Contrastive Learning Model

We provide the commands for the following task: Molecule Generation.

To train the score models, first modify config/$cl{task}_main.yaml accordingly, such as dr for drug response, then run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type cl{task}_train --config ${train_config} --seed ${seed}

for example,

CUDA_VISIBLE_DEVICES=0 python main.py --type cldr_train --config cldr_train --seed 42

and

CUDA_VISIBLE_DEVICES=0,1 python main.py --type cldt_train --config cldt_train --seed 42

5. Training Condition Model

We provide the commands for the following task: Molecule Generation.

To train the score models, first modify config_control/{task}_train{_multi_data}.yaml accordingly, such as drp for drug response, _multi_data is default， then run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type train --config config_control/${train_config} --seed ${seed}

for example,

CUDA_VISIBLE_DEVICES=0 python main.py --type control_drp --config config_control/drp_train --seed 42

CUDA_VISIBLE_DEVICES=0 python main.py --type multidata_control_drp --config config_control/drp_train --seed 42

and

CUDA_VISIBLE_DEVICES=0,1 python main.py --type multidata_control_dta --config config_control/dta_train --seed 42

6. Generation and Evaluation

To generate graphs using the trained score models, run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type {task}_condition_sample --config sample_{dataset}

or

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type sample --config sample_zinc250k

Pretrained checkpoints

We provide checkpoints of the pretrained models on the checkpoints/ directory, which are used in the main experiments.

• ['GDSCv2', 'QM9']/date-time.pth
• QM9/qm9.pth
• ZINC250k/zinc250k.pth

We have uploaded a zip pack of code that can be used for conditional sampling directly, which can be downloaded (https://drive.google.com/file/d/15He7YHE39wT9tdNcqSOPR-bvzvO_Isr9/view?usp=drive_link).

cd ./RFMG_Sampling
CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py

rmcd's Issues

About the paper?

May I ask where the link to this paper is? I cannot find the title of this paper.