Structure-based drug design by denoising voxel grids
This addresses the challenge of structure-based drug design for pharmaceutical research, representing an incremental improvement over existing methods.
The paper tackles the problem of generating 3D molecules conditioned on protein structures for drug design, resulting in a model that is simpler to train, faster to sample from, and achieves better performance with more diverse molecules, fewer steric clashes, and higher binding affinity in benchmarks.
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/.