Abstract
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. This proposed method pioneers the application of ControlNet in diffusion model-based drug development. Its ability to generate drug-like compounds from 3D conformations is prominent due to its capability to bypass Open Babel post-processing and integrate bond details and molecular information. RESULTS: For the gold standard QM9 dataset, CoDNet outperforms existing state-of-the-art methods with a validity rate of 99.02%. This competitive performance underscores the precision and efficacy of CoDNet's drug design, establishing it as a significant advancement with great potential for enhancing drug development initiatives. AVAILABILITY AND IMPLEMENTATION: https://github.com/CoDNet1/EDM_Custom.