DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design

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ID: 329938
2026
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Abstract
MOTIVATION: Proteolysis-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. RESULTS: To 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 achieves the best or highly competitive performance across Validity, Uniqueness, and Recovery. The Case study further shows that DesignMaster consistently achieves improved geometric agreement with the reference PROTAC conformations across both the 6W7O and 6HAY complexes, highlighting its potential for practical structure-guided and property-aware PROTAC design. AVAILABILITY: The source code and datasets are available at https://github.com/ABILiLab/DesignMaster.
Reference Key
openalex_W7165192704 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Binze Shi, Yi Hao, Jie Liu, Tong Pan, Luke Isbel, Michael J. Roy, Ashley P. Ng, Xuequn Shang, Fuyi Li
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag714
URL
https://doi.org/10.1093/bioinformatics/btag714
Keywords Keywords not found

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