Structure-based drug design by denoising voxel grids
<|reference_start|>Structure-based drug design by denoising voxel grids: We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverages a 3D voxel-denoising network for learning and generation. We extend the neural empirical Bayes formalism (Saremi & Hyvarinen, 2019) to the conditional setting and generate structure-conditioned molecules with a two-step procedure: (i) sample noisy molecules from the Gaussian-smoothed conditional distribution with underdamped Langevin MCMC using the learned score function and (ii) estimate clean molecules from the noisy samples with single-step denoising. Compared to the current state of the art, our model is simpler to train, significantly faster to sample from, and achieves better results on extensive in silico benchmarks -- the generated molecules are more diverse, exhibit fewer steric clashes, and bind with higher affinity to protein pockets. The code is available at https://github.com/genentech/voxbind/.<|reference_end|>
@article{pinheiro2024structure-based,
    title={Structure-based drug design by denoising voxel grids},
    author={Pedro O. Pinheiro and Arian Jamasb and Omar Mahmood and Vishnu Sresht
  and Saeed Saremi},
    journal={arXiv preprint arXiv:2405.03961},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03961},
    primaryClass={cs.LG q-bio.BM}
}