DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

Abstract

In molecular optimization, one popular way is R-groups decoration on molecular scaffolds, and many efforts have been put to generate R-groups based on deep generative models. However, these methods mostly use information of known binding ligands, without fully utilizing target structure information. In this study, we proposed a new method, DiffDec, to involve three-dimensional pocket constraints by a modified diffusion technique for optimizing molecules through molecular scaffold decoration. For an end-to-end generation of R-groups with different sizes, we designed a novel fake atom mechanism. DiffDec was shown able to generate structure-aware R-groups, and simultaneously generate multiple R-groups for one scaffold on different growth anchors. The growth anchors could be provided by users or automatically determined by our model. DiffDec achieved R-group recovery rates of 69.67% and 45.34% in the single and multiple R-group decoration tasks, respectively, and these values were significantly higher than competing methods (37.33% and 26.85%). According to the molecular docking study, our decorated molecules obtained better average binding affinity than baseline methods. The docking pose analysis revealed that DiffDec could decorate scaffolds with R-groups that exhibited improved binding affinities and more favourable interactions with the pocket. These results demonstrated the potential and applicability of DiffDec in real-world scaffold decoration for molecular optimization.