ArticlePLoS computational biology2022
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Review
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Single-cell hit calling in high-content imaging screens with Buscar.bioRxiv : the preprint server for biology · 2026Article
- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- Stability of non-canonical nucleic acid structure as a potential modulator of cell fate.Nucleic acids research · 2026Review
- Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages.Bioengineering (Basel, Switzerland) · 2026Article
- Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal.Bioinformatics advances · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- AI-Driven Polypharmacology in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.Molecular oncology · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Self-supervision advances morphological profiling by unlocking powerful image representations.Scientific reports · 2025Article
- Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation.International dental journal · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- uniDINO: Assay-independent feature extraction for fluorescence microscopy images.Computational and structural biotechnology journal · 2025Article
- A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology.Current research in toxicology · 2025Review
- MorphoDiff: Cellular Morphology Painting with Diffusion Models.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, β-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.
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