BioinformaticsBioinformaticsPeer-reviewed journal article
DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design
Binze Shi, Yi Hao, Jie LiuiD, Tong PaniD, Luke IsbeliD, Michael J. RoyiD, Ashley P. NgiD, Xuequn ShangiD, Fuyi LiiD
Abstract
MotivationProteolysis-targeting chimeras (PROTACs) enable targeted protein degradation through ternary complex formation with E3 ubiquitin ligase. However, the rational design of PROTACs remains highly challenging due to limited structure-activity relationship data and the vast conformational diversity of linkers. Existing computational approaches can be broadly divided into structure-based ternary modelling methods and fragment-based linker generation models. Although these approaches have advanced PROTAC design, they typically neglect key physicochemical constraints and linker-length control during the generation process, causing the generated PROTACs to lack balanced structural properties required for effective ternary complex formation with drug-like characteristics. ResultsTo address these limitations, we propose DesignMaster, a diffusion-based generative framework that explicitly incorporates linker length and physicochemical properties as controllable conditioning signals. DesignMaster employs an E(3)-equivariant graph Transformer with a gated multi-condition fusion module to inject linker length and physicochemical constraints throughout the diffusion process, enabling fine-grained and constraint-aware molecular generation. Experiments on PROTAC-DB 2.0 and 3.0 demonstrate that DesignMaster outperforms state-of-the-art baselines, with a 3.2% improvement in validity and a 34.4% improvement in recovery. The Case study shows DesignMaster achieves a 51.78% reduction in RMSD when predicting the linker of PROTAC BCPyr targeting 6W7O, highlighting its potential for practical structure-guided PROTAC design. AvailabilityThe source code and datasets are available at https://github.com/ABILiLab/DesignMaster.
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 36th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Fragment Linker Prediction Using Deep Encoder-Decoder Network for PROTAC Drug Design
- SynPROTAC: synthesizable PROTACs design through synthesis constrained generative model and reinforcement learning
Mingyuan Xu, Chaoming Huang, … Jinping Lei·JACS AuPeer-reviewed·Jul 7, 2026
- FlexiTAC enables controllable PROTAC linker generation across diverse structural settings using a Bayesian flow network with posterior guidance
Yuzhang Li, Yupu Zhao, … Duanhua Cao·bioRxiv·Aug 30, 2026
- PROTACable is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning to Automate the De Novo Design of PROTACs
Hazem Mslati, Francesco Gentile, … Artem R Cherkasov·Journal of Chemical Information and ModelingPeer-reviewed·Mar 20, 2024
- Pro-GAT: Reconnecting Fragmented PROTACs Using Graph Attention Transformer
Saahithi Vemuri, Laxmi Priya Bijigiri, … Vani Kondaparthi·Journal of Chemical Information and ModelingPeer-reviewed·Aug 31, 2026
- Efficient Sampling of PROTAC-Induced Ternary Complexes
Hongtao Zhao, Stefan Schießer, … Werngard Czechtizky·bioRxiv·Oct 30, 2024