PDD: Awesome Phenotypic Drug Discovery
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Updated
Oct 17, 2025
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.
Validate the semantic correctness, metadata completeness, provenance and AI readiness of Cell Painting datasets represented in AnnData.
Data repository for Sivagurunathan et al., 2025, "Alternate dyes for image-based profiling assays"
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.
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