Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912967885324288 |
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| author | Lian, Xiaoqing Ma, Pengsen Ma, Tengfeng Ren, Zhonghao Cai, Xibao Cheng, Zhixiang Song, Bosheng Wang, He Pan, Xiang Chen, Yangyang Yuan, Sisi Lin, Chen |
| author_facet | Lian, Xiaoqing Ma, Pengsen Ma, Tengfeng Ren, Zhonghao Cai, Xibao Cheng, Zhixiang Song, Bosheng Wang, He Pan, Xiang Chen, Yangyang Yuan, Sisi Lin, Chen |
| contents | 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 experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening Lian, Xiaoqing Ma, Pengsen Ma, Tengfeng Ren, Zhonghao Cai, Xibao Cheng, Zhixiang Song, Bosheng Wang, He Pan, Xiang Chen, Yangyang Yuan, Sisi Lin, Chen Quantitative Methods Artificial Intelligence Machine Learning 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 experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE. |
| title | Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.15006 |