This code accompanies the paper on soft value-based decoding in diffusion models, where the objective is to maximize downstream reward functions in diffusion models. In this implementation, we focus on designing biological sequences, such as DNA (enhancers) and RNA (5'UTRs). For images, refer to here. For molecular tasks, refer to here.

We will make molecule/protein generation part publicaly available soon. The algorithm is summarized in the following table/figure.

We prepared the pre-trained model using the masked diffusion model Sahoo et.al, 2024 and the dataset in Gosai et al., 2023. We aim to generate natural enhancers with higher activities in HepG2 with soft-value based decoding. The first one is SVDD-MC. The second one corresponds to SVDD-PM. Then, generated $r$'s are saved in the log folder. Regarding evaluation of generated samples, you could refer to eval_simple.ipynb

CUDA_VISIBLE_DEVICES=1 python decode.py --load_checkpoint_path artifacts/DNA_value:v0/human_enhancer_diffusion_enformer_7_11_1536_16_ep10_it3500.pt --task dna --sample_M 10

CUDA_VISIBLE_DEVICES=2 python decode_tweedie.py --load_checkpoint_path artifacts/DNA_value:v0/human_enhancer_diffusion_enformer_7_11_1536_16_ep10_it3500.pt --task dna --sample_M 10 --tweedie True

We prepared the pre-trained model using the masked diffusion model Sahoo et.al, 2024 and the dataset in Sample et al., 2019. We aim to generate natural enhancers with higher activities in HepG2 with soft-value based decoding. The first one is SVDD-MC. The second one corresponds to SVDD-PM. Then, generated $r$'s are saved in the log folder. Regarding evaluation of generated samples, you could refer to eval_simple.ipynb

CUDA_VISIBLE_DEVICES=3 python decode.py --load_checkpoint_path artifacts/RNA_MRL_value:v0/rna_MRL_diffusion_convgru_6_64_512_ep10_it2800.pt --reward_name MRL --task rna --sample_M 10

CUDA_VISIBLE_DEVICES=1 python decode_tweedie.py --load_checkpoint_path artifacts/RNA_MRL_value:v0/rna_MRL_diffusion_convgru_6_64_512_ep10_it2800.pt --reward_name MRL --task rna --sample_M 10 --tweedie True

Run

conda create -n biodif python=3.9
conda activate biodif
pip install -r requirements.txt 
`

Then, to get pre-trained diffusoin models/oracles from W&B, run

python allmodels/model_load.py

If you find this work useful in your research, please cite:

@article{li2024derivative,
  title={Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding},
  author={Li, Xiner and Zhao, Yulai and Wang, Chenyu and Scalia, Gabriele and Eraslan, Gokcen and Nair, Surag and Biancalani, Tommaso and Regev, Aviv and Levine, Sergey and Uehara, Masatoshi},
  journal={arXiv preprint arXiv:2408.08252},
  year={2024}
}