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

👩‍🍳 Recipe repository for image-based profiling of Pooled Cell Painting experiments

Predicting pharmacodynamic responses to cancer drugs using cell morphology

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.

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

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

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.

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

cpg0011-lipocyteprofiler - Batch1 and Batch3

Locked reanalysis of matched ORF and CRISPR Cell Painting profiles with manuscript, source data, and compact evidence bundle.