Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening

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ID: 322518
2026
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Abstract
Abstract Motivation The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of modality-specific variation. Across held-out retrieval, scaffold-split and UMAP-clustering virtual-screening benchmarks, DECODE improves functional retrieval and early active-compound prioritization over baselines. Availability and implementation The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.
Reference Key
openalex_W7171048076 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiaoqing Lian, Pengsen Ma, Tengfeng Ma, Zhonghao Ren, Xibao Cai, Zhixiang Cheng, Bosheng Song, He Wang, Xiang Pan, Yangyang Chen, Sisi Yuan, Le Chen
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag517
URL
https://doi.org/10.1093/bioinformatics/btag517
Keywords Keywords not found

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