DecompOpt: Controllable and Decomposed Diffusion Fashions for Construction-based MolecularOptimization
Authors: Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu
Summary: Just lately, 3D generative fashions have proven promising performances in structure-based drug design by studying to generate ligands given goal binding websites. Nevertheless, solely modeling the target-ligand distribution can hardly fulfill one of many major objectives in drug discovery — designing novel ligands with desired properties, e.g., excessive binding affinity, simply synthesizable, and so forth. This problem turns into significantly pronounced when the target-ligand pairs used for coaching don’t align with these desired properties. Furthermore, most current strategies intention at fixing textit{de novo} design job, whereas many generative eventualities requiring versatile controllability, akin to R-group optimization and scaffold hopping, have acquired little consideration. On this work, we suggest DecompOpt, a structure-based molecular optimization technique primarily based on a controllable and decomposed diffusion mannequin. DecompOpt presents a brand new technology paradigm which mixes optimization with conditional diffusion fashions to attain desired properties whereas adhering to the molecular grammar. Moreover, DecompOpt affords a unified framework masking each textit{de novo} design and controllable technology. To realize so, ligands are decomposed into substructures which permits fine-grained management and native optimization. Experiments present that DecompOpt can effectively generate molecules with improved properties than sturdy de novo baselines, and exhibit nice potential in controllable technology duties.