Evidence map›Paper›PMID 41439702›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Flexible protein-ligand docking with diffusion-based side-chain packing.

Runze Zhang, Xinyu Jiang, Duanhua Cao, Zhaokun Wang, Jie Yu, Mingan Chen, Zhehuan Fan, Xiangtai Kong, Jiacheng Xiong, Zimei Zhang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Runze Zhang *Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Xinyu Jiang *Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.ORCID 0009-0000-1262-9947
Duanhua Cao *Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.ORCID 0009-0006-0140-7998
Zhaokun Wang *Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Jie YuDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.ORCID 0009-0001-5966-2610
Mingan ChenDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zhehuan FanDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Xiangtai KongDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.ORCID 0000-0002-6257-2560
Jiacheng XiongDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zimei ZhangDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Wei ZhangDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Shengkun NiDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Yitian WangDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Minda LiaoDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Shenghua GaoSchool of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Sulin ZhangDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Mingyue ZhengDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.ORCID 0000-0002-3323-3092

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding protein structure and dynamics is crucial for basic biology and drug design. Conventional methods often provide static conformations that inadequately capture protein flexibility. We present PackDock, a framework that integrates deep learning and physics-based modeling to represent protein-ligand interactions. PackDock's core, PackPocket, uses diffusion models to sample diverse binding pocket conformations and predict ligand-induced changes. We validate PackDock through side-chain packing, redocking, and cross-docking experiments, demonstrating its ability to address protein flexibility challenges. In a real-world application, PackDock identified nanomolar affinity compounds with unreported scaffolds for the protein of interest. Additionally, it revealed key amino acid conformational changes, offering insights into protein-ligand interactions. By accurately predicting complex conformations in various scenarios, PackDock enhances our understanding of protein dynamics and provides perspectives for both basic biological research and drug discovery efforts.

Indexed as

Molecular Docking SimulationProteinsBinding SitesDeep LearningDiffusionLigandsProtein BindingProtein ConformationLigandsProteinsmachine learningmolecular dockingprotein structure prediction

Identifiers

PMID41439702
PMCPMC12772217

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.