DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
<|reference_start|>DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization: Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties, e.g., high binding affinity, easily synthesizable, etc. This challenge becomes particularly pronounced when the target-ligand pairs used for training do not align with these desired properties. Moreover, most existing methods aim at solving \textit{de novo} design task, while many generative scenarios requiring flexible controllability, such as R-group optimization and scaffold hopping, have received little attention. In this work, we propose DecompOpt, a structure-based molecular optimization method based on a controllable and decomposed diffusion model. DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar. Additionally, DecompOpt offers a unified framework covering both \textit{de novo} design and controllable generation. To achieve so, ligands are decomposed into substructures which allows fine-grained control and local optimization. Experiments show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines, and demonstrate great potential in controllable generation tasks.<|reference_end|>
@article{zhou2024decompopt:,
    title={DecompOpt: Controllable and Decomposed Diffusion Models for
  Structure-based Molecular Optimization},
    author={Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan
  Gu},
    journal={arXiv preprint arXiv:2403.13829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13829},
    primaryClass={q-bio.BM cs.LG}
}