Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

在早期药物发现中，识别靶标的活性化合物是一项耗时和资源密集型的任务。使用形态学特征进行准确的生物活性预测可以简化该过程，从而实现更小、更集中的化合物筛选。我们研究了深度学习对未精炼的单浓度活性读数和细胞绘制数据的潜力，以预测 140 种不同检测中的化合物活性。我们观察到平均 ROC-AUC 为 0.744 ± 0.108，其中 62% 的检测达到 ≥0.7，30% ≥0.8，7% ≥0.9。在许多情况下，仅使用明场图像而不是多通道荧光图像即可实现高预测性能。综合分析表明，基于细胞绘画的生物活性预测在各种检测类型、技术和靶标类别中均具有稳健性，其中基于细胞的检测和激酶靶标特别适合预测。实验验证证实了活性化合物的富集。我们的研究结果表明，根据细胞绘画数据训练的模型与一小组单浓度数据点相结合，可以可靠地预测跨不同目标和检测的化合物库的活性，同时保持高命中率和支架多样性。这种方法有可能减少筛查活动的规模，节省时间和资源，并能够通过更复杂的检测进行初步筛查。