Structure–function conditioned molecular generation with PocketGPT enables prospective discovery of PARP1 inhibitors

Rong Qian; Jilong Duan; Xiaoping Liu; Wenxuan Wang; Tingfei Zhu; Dejun Jiang; Jin Liu; Youchao Deng; Aiping Lyu; Dongsheng Cao

doi:10.59717/j.xinn-drugdisc.2026.100028

The Innovation Drug Discovery > 2026 Vol. 1 > No. 2 > 100028

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Structure–function conditioned molecular generation with PocketGPT enables prospective discovery of PARP1 inhibitors

1.
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
2.
School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, China
3.
Institute of systems medicine and health sciences, Hong Kong Baptist University, Hong Kong SAR 999077, China
4.
These authors contributed equally to this work

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Corresponding authors: dengyouchao1206@163.com (Y.D.); aipinglu@hkbu.edu.hk (A.L.); oriental-cds@163.com (D.C.)
Received Date: March 30, 2026

Accepted Date: July 07, 2026

Published Online: July 15, 2026

The Innovation Drug Discovery 1(2), https://doi.org/10.59717/j.xinn-drugdisc.2026.100028 | Cite this article

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1. PocketGPT uses protein pocket and functional information to guide controllable molecule design.
  
  It integrates pocket context, molecular properties, fragments, and interactions to enrich promising candidates.
  
  A prospective poly(ADP-ribose) polymerase 1 (PARP1) study yielded an antitumor nanomolar inhibitor.

ABSTRACT

ABSTRACT

Structure-based molecular generation has become increasingly prominent in drug discovery; however, many generated binders still fail to satisfy the physicochemical and functional constraints required for pharmacological viability. Here, we present PocketGPT, a pocket-conditioned generative framework that demonstrates structure-aware molecular generation can be effectively enabled without explicit three-dimensional ligand construction, by jointly conditioning protein pocket context and medicinal-chemistry constraints during generation. By co-conditioning physicochemical property targets, fragment priors, and protein–ligand interaction fingerprints, PocketGPT enables structure–function co-generative control, allowing directional modulation of drug-like properties while partially retaining binding-relevant interaction patterns across diverse targets. In a prospective design–make–test campaign targeting poly(ADP-ribose) polymerase 1 (PARP1), PocketGPT identified a novel inhibitor with nanomolar binding affinity (K_d = 4.88 nM) and favorable tolerability in mice. Together, these results demonstrate that joint structural and pharmacological conditioning, embedded directly in the generative process, constitutes a viable and controllable representation for structure-aware molecular generation, offering a complementary pathway to geometry-first approaches for producing experimentally actionable chemical matter.

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Qian R., Duan J., Liu X., et al. (2026). Structure–function conditioned molecular generation with PocketGPT enables prospective discovery of PARP1 inhibitors. The Innovation Drug Discovery 1:100028. https://doi.org/10.59717/j.xinn-drugdisc.2026.100028

Qian R., Duan J., Liu X., et al. (2026). Structure–function conditioned molecular generation with PocketGPT enables prospective discovery of PARP1 inhibitors. The Innovation Drug Discovery 1:100028. https://doi.org/10.59717/j.xinn-drugdisc.2026.100028

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Structure–function conditioned molecular generation with PocketGPT enables prospective discovery of PARP1 inhibitors

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