Phenotypic screening paired with AI-powered tools is a mighty tool in drug discovery because it captures whole cell responses in an unbiased way, revealing mechanisms and targets that hypothesis-driven methods might miss. The foundation of making this possible is clean, complete, and consistent data.
Here is a practical guide to help you make the most of your screening data, from assay development to formatting metadata.
1. Start With Robust Assay Development
Great data starts at the bench. AI models amplify signals, but they can also amplify noise. That is why proper assay development is the foundation of reliable phenotypic screening.
Data quality depends on the assay robustness: performance and reproducibility. So make sure to use as many resources as needed, and take the time to optimize all parameters. This will ensure that the money spent on high-throughput experiments is not lost.
Key priorities to have in mind:
- Choose a biologically relevant cell model compatible with high-throughput formats.
- Adjust seeding cell density to allow accurate single-cell segmentation.
- Optimize incubation conditions to reduce plate effects.
2. Tune Image Acquisition Parameters
Using high-content imaging platforms, you capture thousands of cellular images across many wells and plates. Automated microscopy ensures consistency, while multiplexed channels allow detection of diverse subcellular features.
Poor image quality at this stage means bad results down the line. Fit image acquisition parameters by:
- Adjusting exposure time – overexposed images will not allow you to see the intensity changes.
- Setting up the correct offset from the autofocus – out-of-focus images will not accurately represent the real shape and texture of the cells.
- Capturing an optimal number of images per well – snap enough cells to obtain a good representation of the cell population.
3. Follow Best Practices During Screening
Once your assay is ready, the screening should generate good-quality data; however, this phase must be carefully controlled to ensure consistency and minimize variability.
Recommendations:
- Automate dispensing and imaging steps to reduce human error and increase consistency. However, employ expert oversight to correct the mistakes and supply domain knowledge, especially for complex morphological patterns.
- Keep plates, reagents and cell batches consistent (from the same lot) to minimize the risk of batch effects.
- Include positive and negative controls on every plate to monitor assay performance.
- Randomize sample positions across the plate to avoid positional bias.
- Include replicates to detect reproducibility better, matching across conditions, and support robust downstream modeling – use as many as possible.
- Include shared “anchor” samples across batches to allow for robust batch correction and cross-plate comparability.
- AI models demand labelled data for training – ensure your screening library contains annotated compounds.
4. Prepare Metadata That Works For AI
AI models require structured, machine-readable tabular formats such as .csv. Avoid merged cells or multi-row headers, and maintain a uniform table size across datasets.
Even high-quality images will become unusable if associated metadata is incomplete or inconsistent. Ensure all the necessary metadata is included and properly formatted.
Key metadata to include:
- Unique identifiers: plate and well IDs.
- Perturbation information: unique perturbation IDs and a clear description of them (e.g., SMILES or InChI for small molecules, UniProt ID for genetic perturbations) to allow multimodal analysis.
- Experimental conditions: cell line, passage number, compound dose, dyes, and incubation time.
- Imaging parameters: microscope, magnification, channels.
- Plate maps and layouts: to capture positional information for quality control.
- Operator information: person who performed the assay.
5. Analyze Data With PhenAID
Next, image analysis pipelines come into the scene. Traditionally, this involves segmenting individual cells and extracting hundreds or thousands of features covering shape, texture, intensity, granularity, etc. The features must then be normalized to correct for drift and staining variability between plates and batches.
Open-source tools like CellProfiler or KNIME provide a solid foundation for segmentation, feature extraction, and basic exploration of phenotypic data. However, Ardigen’s phenAID goes further. Leveraging state-of-the-art computer vision and deep learning, it extracts high-dimensional features from high-content screening images.
Our tool leverages advanced AI algorithms to predict the compound Mode of Action and biological properties, identify high-quality hits, perform image-based virtual screening, and accelerate lead optimization. It can also integrate phenotypic profiles with omics data and chemical structures to enhance the accuracy and robustness of predictions.
Such advanced analytics also amplifies the impact of input data quality: biases, drifts, or signal inconsistencies cannot always be corrected and will affect the quality of predictions. For reliable results, ensure the input data is as clean, complete, and standardized as possible.
6. Do Not Skip Quality Control
Data quality must be monitored at every step of a high-content screening project. Even with an optimized assay and carefully executed experiments, technical issues may happen that are only detectable at the very end.
Before starting any downstream analysis, ensure the data are suitable. Check whether the obtained cell count matches expectations and identify any blurred images, debris, or signs of contamination. The checklist below can help assess data readiness for AI-driven analysis.
Is my data ready for AI analysis? Short checklist:
Checkpoint | Criteria |
|---|---|
Image quality | The dataset is devoid of images exhibiting blurriness, debris, or contamination |
Cell segmentation | Observed cell count corresponds to the cell density used in the experiment. |
Assay window | Negative and positive controls are visibly separated in the 2D projection of extracted features. |
Plate effects | Heat maps for cell count and selected features do not show any systematic patterns. |
Batch effect | Plates and batches do not cluster together in a 2D projection of normalized features. |
Metadata | Respective metadata contains complete, machine-readable information about well content and experimental conditions. |
7. Think Ahead: Enable Multimodal and FAIR Analysis
Following FAIR (Findable, Accessible, Interoperable, Reusable) principles from the beginning enables better reproducibility, scalability, and integration of your phenotypic platform. To achieve this:
- Store data in interoperable formats to facilitate analysis across platforms.
- Use controlled vocabularies where possible to standardize terminology.
- Ensure all identifiers are unique and traceable for consistent referencing.
- Link raw data to metadata tables to maintain transparency.
Final Thoughts
AI uncovers biologically meaningful patterns only when datasets are carefully prepared. By combining robust assay design, standardized data processing, and metadata structured for machine learning, you unlock the full potential of phenotypic screening and platforms like phenAID.
Need support? Ardigen’s expert teams in data science and biology can help ensure your project is optimally designed and executed, setting your group up for reliable results from the start.
Author: Martyna Piotrowska |
Technical editing: Ardigen experts: Magdalena Otrocka PhD and Nasim Jamali PhD
Bibliography:
- Cimini BA, Chandrasekaran SN, Kost-Alimova M, et al. Optimizing the Cell Painting assay for image-based profiling. Nature Protocols. 1981-2013 [Updated: 2023 Jun 21; cited 2025 Aug 28]. https://doi.org/10.1038/s41596-023-00840-9.
- Markossian S, Grossman A, Baskir H, et al., editors. Assay Guidance Manual [Internet]. Bethesda (MD): Eli Lilly & Company and the National Center for Advancing Translational Sciences; 2004- [cited 2025 Aug 28]. https://www.ncbi.nlm.nih.gov/books/NBK53196/
