Bushiri Pwesombo David, Beese Carsten, Schmied Christopher, Sun Han
Research Unit Structural Chemistry and Computational Biophysics, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin 13125, Germany.
Institute of Chemistry, Technische Universität Berlin, 10623 Berlin, Germany.
J Chem Inf Model. 2025 Jan 27;65(2):528-543. doi: 10.1021/acs.jcim.4c00835. Epub 2025 Jan 6.
Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning. SemiSupCon enhances downstream prediction performance of classifying MeSH pharmacological classifications from PubChem, as well as mode of action and biological target annotations from the Drug Repurposing Hub across two publicly available Cell Painting data sets. Notably, our approach has effectively predicted the biological activities of several unannotated compounds, and these findings were validated through literature searches. This demonstrates that our approach can potentially expedite the exploration of biological activity based on Cell Painting image data with minimal human intervention.
形态学分析最近在识别小分子的生物活性方面显示出巨大潜力。除了最近提出的用于从细胞绘画图像数据预测生物活性的全监督和自监督机器学习方法外，我们在此引入一种半监督对比（SemiSupCon）学习方法。这种方法结合了在监督对比学习中使用生物注释的优势以及通过自监督对比学习利用大量未注释图像数据集的优势。SemiSupCon提高了从PubChem分类MeSH药理学分类以及从药物再利用中心对两个公开可用的细胞绘画数据集的作用模式和生物靶点注释进行分类的下游预测性能。值得注意的是，我们的方法有效地预测了几种未注释化合物的生物活性，并且这些发现通过文献检索得到了验证。这表明我们的方法有可能在最少的人工干预下加速基于细胞绘画图像数据的生物活性探索。
