Accelerating discovery of bioactive ligands with pharmacophore-informed generative models

Weixin Xie, Jianhang Zhang, Qin Xie, Chaojun Gong, Yuhao Ren, Jin Xie, Qi Sun, Youjun Xu (), Luhua Lai () and Jianfeng Pei ()
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Weixin Xie: Peking University
Jianhang Zhang: Infinite Intelligence Pharma
Qin Xie: Infinite Intelligence Pharma
Chaojun Gong: Infinite Intelligence Pharma
Yuhao Ren: Peking University
Jin Xie: Peking University
Qi Sun: Peking University
Youjun Xu: Infinite Intelligence Pharma
Luhua Lai: Peking University
Jianfeng Pei: Peking University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Deep generative models have advanced drug discovery but often generate compounds with limited structural novelty, providing constrained inspiration for medicinal chemists. To address this, we develop TransPharmer, a generative model that integrates ligand-based interpretable pharmacophore fingerprints with a generative pre-training transformer (GPT)-based framework for de novo molecule generation. TransPharmer excels in unconditioned distribution learning, de novo generation, and scaffold elaboration under pharmacophoric constraints. Its unique exploration mode could enhance scaffold hopping, producing structurally distinct but pharmaceutically related compounds. Its efficacy is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably, three out of four synthesized PLK1-targeting compounds show submicromolar activities, with the most potent, IIP0943, exhibiting a potency of 5.1 nM. Featuring a new 4-(benzo[b]thiophen-7-yloxy)pyrimidine scaffold, IIP0943 also has high PLK1 selectivity and submicromolar inhibitory activity in HCT116 cell proliferation. TransPharmer offers a promising tool for discovering structurally novel and bioactive ligands.

Date: 2025
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DOI: 10.1038/s41467-025-56349-0

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