lei, j. | bioRxiv Science

bioRxiv2025

SynPROTAC: synthesizable PROTACs design through synthesis constrained generative model and reinforcement learning

Protein hydrolysis targeting chimeric (PROTAC) has emerged as a promising technology in degrading disease-related proteins for drug design. Recent deep generative models can accelerate PROTAC design, but the generated molecules are often difficult to synthesize. Here we develop SynPROTAC model, which employs Graphormer encoded warhead or E3 ligand as input, and autoregressively samples reaction templates and building blocks through transformer based decoder for PROTAC construction. The model is also fine-tuned via reinforcement learning for generating PROTACs with favorable binding properties. The comprehensive evaluations indicated that SynPROTAC is capable of generating novel PROTACs with feasible synthetic routes, reasonable physico-chemical and binding related properties.

bioinformatics↗

bioRxiv2023

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

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.

bioinformatics↗