Awesome Image-based Phenotypic Drug Discovery

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Datasets

[Datasets] JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations (Arxiv) [paper] [dataset]
[Datasets and Benchmark] Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations (Nature Methods) [paper] [code]
[DeepProfiler] Learning representations for image-based profiling of perturbations (Nature Communications) [paper] [code]

[Survey] Deep learning in image-based phenotypic drug discovery (Trends in Cell Biology) [paper]
[Survey] Artificial intelligence for high content imaging in drug discovery (Current Opinion in Structural Biology) [paper]
[Multimodal] Multimodal data fusion for cancer biomarker discovery with deep learning (Nature Machine Intelligence) [paper]
Predicting compound activity from phenotypic profiles and chemical structures (Nature Communications) [paper] [code]
Deep representation learning determines drug mechanism of action from cell painting images (Digital Discovery) [paper] [code]
[Pycytominer] Reproducible image-based profiling with Pycytominer (Arxiv) [paper] [code]

[Datasets and Benchmark] High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations (Cancer Cell) [paper] [code]
[Datasets and Benchmark] Pan-cancer integrative histology-genomic analysis via multimodal deep learning (Nature Methods) [paper] [code]
Morphology and gene expression profiling provide complementary information for mapping cell state (Cell Systems) [paper] [code]
[Dataset] Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts (Nature Communications) [paper] [dataset]
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection (Communications Biology) [paper] [code]

[HGGEP] Gene Expression Prediction from Histology Images via Hypergraph Neural Networks (Arxiv) [paper] [code]
[iStar] Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology (Cell Reports Medicine) [paper] [code]
[IGI-DL] Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system (Nature Biotechnology) [paper] [code]
[BLEEP] Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning (NIPS 2023) [paper] [code]
[DeepPT] Prediction of cancer treatment response from histopathology images through imputed transcriptomics (Journal of Clinical Oncology) [paper] [code]
[SEPAL] SEPAL: Spatial Gene Expression Prediction from Local Graphs (ICCV 2023) [paper] [code]
[Hist2ST] Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks (Briefings in Bioinformatics) [paper] [code]
[HisToGene] Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors (Biorxiv) [paper] [code]
[ST-Net] Integrating spatial gene expression and breast tumour morphology via deep learning (Nature biomedical engineering) [paper] [code]