PDD: Awesome Phenotypic Drug Discovery
Predicting Cell Health with Morphological Profiles
Processed Cell Painting Data for the LINCS Drug Repurposing Project
[NeurIPS 2025] CellCLIP – Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
Accompanying code for Image2Omics
Predicting pharmacodynamic responses to cancer drugs using cell morphology
Recipe for profile creation for pooled image-based/morphological profiling (including Cell Painting) experiments
Benchmarking data processing strategies for Cell Painting data of NF1 Schwann cells. See analysis repository (https://github.com/WayScience/NF1_SchwannCell_data_analysis) for information on how the data was interpreted.
[CVPRW 2024] Learning interpretable single-cell morphological profiles from 3D Cell Painting z-stacks
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
Image-based profiling and machine learning to predict failing vs. non-failing cardiac fibroblasts
Single cell analysis of the JUMP Cell Painting consortium pilot data (cpg0000)
A morphology scout for discovering representative cell phenotypes with Stable Diffusion
🛠️ Use me to version control Pooled Cell Painting data and processing pipelines
Leakage-aware benchmark and research prototype for natural-language retrieval over Cell Painting perturbation profiles.
Framework for end-to-end processing of high throughput microscopy.
Text-supervised contrastive learning that aligns Cell Painting microscopy embeddings with biological perturbation descriptions for cross-modal perturbation matching.
Anomaly detection for high-content image-based phenotypic cell profiling
Validate the semantic correctness, metadata completeness, provenance and AI readiness of Cell Painting datasets represented in AnnData.
Cell Painting-based phenotypic similarity search pipeline for drug mechanism discovery, comparing CNN-derived morphology embeddings with mechanism labels and chemical structure similarity.
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