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Structure-based Drug Design with Equivariant Diffusion Models. (arXiv:2210.13695v1 [q-bio.BM])
Oct. 26, 2022, 1:11 a.m. | Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bro
cs.LG updates on arXiv.org arxiv.org
Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets.
Traditional SBDD pipelines start with large-scale docking of compound libraries
from public databases, thus limiting the exploration of chemical space to
existent previously studied regions. Recent machine learning methods approached
this problem using an atom-by-atom generation approach, which is
computationally expensive. In this paper, we formulate SBDD as a 3D-conditional
generation problem and present DiffSBDD, an E(3)-equivariant 3D-conditional
diffusion model …
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