Polypharmacological Target-Aware Molecule Generation

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ID: 332269
2026
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Abstract
MOTIVATION: Designing compounds capable of modulating multiple target proteins simultaneously (i.e., polypharmacology) is critical for treating complex diseases like cancer and neurodegenerative disorders. However, AI-driven multi-target drug design remains severely hindered by the scarcity of multi-target 3D structural data and the inefficiency of traditional "generate-then-filter" pipelines. There is a pressing need for scalable methods that can satisfy diverse binding constraints across multiple proteins without relying on scarce structural priors. RESULTS: To overcome these bottlenecks, we reformulate multi-target drug design as a sequence-based generation task and introduce PolyTamGen, an end-to-end autoregressive framework. We first curate a large-scale ligand-centric bioactivity dataset from ChEMBL and BindingDB, comprising 1.6 million unique ligands and 4.9 million bioactivity entries. PolyTamGen encodes target protein sets using ESM3, compresses these high-dimensional features into compact tokens via an attention module, and feeds them into a sequence-based compound generator to produce the final multi-target ligands. We further apply Direct Preference Optimization (DPO) to improve docking-based affinity proxies and drug-likeness. Extensive experiments on dual- and triple-target benchmarks demonstrate that PolyTamGen improves joint success rates over representative baselines while maintaining high chemical validity and diversity. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/bjzgcai/PolyTamGen. The processed data, model checkpoints, and benchmark results are available at Zenodo: https://doi.org/10.5281/zenodo.20594128. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W7221674645 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kehan Wu, Wei Zhang, Han Guo, Renhe Liu, Yu Shi, Peiran Jin, Haiguang Liu, Enhong Chen, Tao Qin, Zequn Liu, Yingce Xia
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag766
URL
https://doi.org/10.1093/bioinformatics/btag766
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