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 repository for image-based profiling of Pooled 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.

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

[CVPRW 2024] Learning interpretable single-cell morphological profiles from 3D Cell Painting z-stacks

Single cell analysis of the JUMP Cell Painting consortium pilot data (cpg0000)

🛠️ Use me to version control Pooled Cell Painting data and processing pipelines

Framework for end-to-end processing of high throughput microscopy.

Anomaly detection for high-content image-based phenotypic cell profiling

Data repository for Sivagurunathan et al., 2025, "Alternate dyes for image-based profiling assays"

RF models for the prediction of cell viability in muscle cells from Cell Painting profiles.

Phenomics perturbation profiling and MoA retrieval from morphological cell-painting profiles (UMAP + HDBSCAN + cosine similarity). POC: LINCS Cell Painting — recall@5 = 3.0× random baseline on 111 compounds across 20 MoA classes.

quickly generate overviews of Cell Painting image plates

cpg0011-lipocyteprofiler - Batch1 and Batch3