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Cited by 1Computational Drug Discovery MethodsSingle-cell and spatial transcriptomicsBioinformatics and Genomic Networks

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scDock: streamlining drug discovery targeting cell–cell communication via scRNA-seq analysis and molecular docking

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Huang, Chen-Hao, Oyang, Yen-Jen, Huang, Hsuan-Cheng, Juan, Hsueh-Fen·2026·Bioinformatics
DOI 10.1093/bioinformatics/btag103PMC PMC12996892

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Abstract

Summary: Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell–cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand–receptor interactions from scRNA-seq data and performs structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell–cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and implementation: scDock is freely available at https://doi.org/10.6084/m9.figshare.31370368 and https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

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Crossref · 10.1093/bioinformatics/btag103

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Figures

Figure 1 Overview of the scDock analytical workflow. The pipeline comprises three principal modules: the core scRNA-seq analysis module, the intercellular communication inference module, and the molecular docking module. These modules are sequentially connected, where the output generated from each module serves as the input for the subsequent module. The core scRNA-seq analysis module performs quality control, normalization, scaling, dimensionality reduction, clustering, integration, and cell type annotation. The intercellular communication inference module identifies key signaling pathways through comparison of communication probabilities across groups and selects potential therapeutic targets based on the implicated ligand–receptor pairs. In the molecular docking module, protein and compound structures are retrieved, preprocessed, and subjected to docking analysis. Candidate compounds are subsequently ranked according to their predicted binding affinities to target proteins, facilitating the identification of drugs that may modulate disease-associated ligand–receptor interactions.
Figure 1 Overview of the scDock analytical workflow. The pipeline comprises three principal modules: the core scRNA-seq analysis module, the intercellular communication inference module, and the molecular docking module. These modules are sequentially connected, where the output generated from each module serves as the input for the subsequent module. The core scRNA-seq analysis module performs quality control, normalization, scaling, dimensionality reduction, clustering, integration, and cell type annotation. The intercellular communication inference module identifies key signaling pathways through comparison of communication probabilities across groups and selects potential therapeutic targets based on the implicated ligand–receptor pairs. In the molecular docking module, protein and compound structures are retrieved, preprocessed, and subjected to docking analysis. Candidate compounds are subsequently ranked according to their predicted binding affinities to target proteins, facilitating the identification of drugs that may modulate disease-associated ligand–receptor interactions.CC BY · PubMed Central

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Metadata

Authors
Huang, C.-H., Oyang, Y.-J., Huang, H.-C., & Juan, H.-F.
Affiliations
  • Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University , Taipei 106,
  • National Taiwan University (TW)
  • National Yang Ming Chiao Tung University (TW)
Corresponding author
  • Huang, Hsuan-Cheng
  • Juan, Hsueh-Fen
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2026-02-28
Volume
42
Issue
3
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btag103
PMC
PMC12996892
OpenAlex
W7133224016
Cited by
1
References
3
Citations by year
2026: 1
FWCI
3.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Science and Technology Council (NSTC 112-2221-E-A49-061-MY3, NSTC 114-2221-E-A49-144-MY3)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
intercellular communication, drug discovery, ligand-receptor interactions, structure-based virtual screening, molecular docking, single-cell RNA sequencing, AlphaFold protein structure, drug target prioritization, bioinformatics pipeline
MeSH terms
RNA-Seq, Single-Cell Gene Expression Analysis, Cell Communication, Humans, Ligands, Software, Sequence Analysis, RNA, Computational Biology, Drug Discovery, Single-Cell Analysis, Molecular Docking Simulation

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CoChat. (2026). Citation check: scDock: streamlining drug discovery targeting cell–cell communication via scRNA-seq analysis and molecular docking. https://app.cochat.ai/papers/10.1093/bioinformatics/btag103
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Huang, C.-H., Oyang, Y.-J., Huang, H.-C., & Juan, H.-F. (2026). scDock: streamlining drug discovery targeting cell–cell communication via scRNA-seq analysis and molecular docking. Bioinformatics. https://doi.org/10.1093/bioinformatics/btag103