Motivation: Structure-based drug design (SBDD) holds promising potential to design ligands with high-binding affinity and rationalize their interaction with targets. By utilizing geometric knowledge of the three-dimensional (3D) structures of target binding sites, SBDD enhances the efficacy and selectivity of therapeutic agents by optimizing binding interactions at the molecular level. Here, we present CoDNet, a novel approach that combines the conditioning capabilities of ControlNet with the potency of the diffusion model to create generative frameworks for molecular compound design.
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