Work

Polypharmacological target-aware molecule generation

Year: 2026

Type: article

Abstract: MOTIVATION: Designing compounds capable of modulating multiple target proteins simultaneously (i.e. polypharmacology) is critical for treating complex diseases such as cancer and neurodegenerative disorders. However, AI-driven multitarget drug design remains severely hindered by the scarcity of multitarget 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 multitarget 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 multitarget ligands. We further apply Direct Preference Optimization 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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btag766