Predicting Cell Health with Morphological Profiles

PDD: Awesome Phenotypic Drug Discovery

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

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

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

Text-supervised contrastive learning that aligns Cell Painting microscopy embeddings with biological perturbation descriptions for cross-modal perturbation matching.

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

A bilingual work-in-progress book on AI-driven phenotypic drug discovery, from high-throughput screening and Cell Painting to machine learning and drug development.

Cell Painting-based phenotypic similarity search pipeline for drug mechanism discovery, comparing CNN-derived morphology embeddings with mechanism labels and chemical structure similarity.

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

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