Related Experiment Video
Updated: Aug 6, 2026
A Fluorescence-based Protocol for Preliminary Screening of Protein Synthesis Inhibitors from Natural Sources
Published on: January 27, 2026
Empowering chemical structures with biological insights for scalable phenotypic virtual screening
Xiaoqing Lian1,2,3, Pengsen Ma1,2,3, Tengfeng Ma1,2,3
1State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
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.
