High-Throughput Transcriptomics for Data Moats in AI-Enabled Drug Discovery
AI-enabled drug discovery remains limited by the scarcity of large-scale, standardized perturbation data that has the biological depth to capture how compounds affect cells across doses, models, and conditions.
Many standard assays still lack the depth, throughput, and reproducibility needed to train AI models that connect compounds to mechanisms of action and toxicity, and support downstream decision-making. As a result, wet lab data generation struggles to keep up with the pace of AI model iteration, limiting AI-native biopharma in their lab-in-the-loop drug discovery and development cycles.
With MERCURIUS™ 1536 DRUG-seq, AI-enabled drug discovery teams can now generate standardized transcriptional compound-perturbation datasets in high-throughput, screening-ready 1536-well plates with the depth, scale, and speed foundational models require.
In this tech note, we showcase the technical performance of MERCURIUS™ 1536 DRUG-seq and suggest that this AI-ready, automatable, transcriptomic data generation technology can help AI-enabled drug discovery teams build extensive perturbation data moats that give adopters the competitive advantage needed to improve foundational model performance and experimentally assess AI-native predictions.
Tech Note Takeaways
MERCURIUS™ 1536 DRUG-seq is a screening-scale transcriptomics data generation platform designed for AI-enabled drug discovery in biopharma. It lets AI-native biopharma research teams build structured, proprietary foundational data moats to train AI models or experimentally test AI-generated predictions at scale.
- Fewer failure points and faster experiment-to-model cycles with an RNA-extraction-free, 2.5-hour workflow, compatible with existing automated HTS pipelines.
- Shallow sequencing without losing gene detection sensitivity lowers cost per data point while still consistently detecting 10,000+ genes at 200K reads/sample across cell types.
- Miniaturized reactions lower cost per well to the level required by foundation-model-scale datasets.
- Intraplate consistency with uniform demultiplexing, read depth, and gene detection across 1536 wells, combined with minimal edge effects, reduces the need for extensive computational correction.
- Teams could generate a compound perturbation dataset of 1 million transcriptomes in as little as 1.5 months for rapid lab-in-the-loop to model iteration cycles.
Questions AI-native teams ask us
Why is proprietary biological data becoming the competitive moat in AI drug discovery, beyond model architecture? Model architectures and compute power are broadly accessible and are not the differentiator they once were. What competitors struggle to replicate is your model training history, including the specific, standardized, high-volume perturbation dataset you’ve generated over time. A data-generation platform that produces reproducible biological data at scale, prospectively, is what turns routine screening into a compounding asset.
What’s my actual cost per datapoint for perturbation data at AI training scale? It depends on assay type, plate format, cell density, and sequencing depth. Major factors that lower cost per datapoint are:
- RNA-extraction-free processing, as it removes a major sample preparation step, lowering per-sample cost and reducing failure points
- Low required sequencing depth, as it maximizes sequencing space without compromising gene detection sensitivity (200K reads/sample still detects 10,000+ genes)
- Low cell input (400–1,000 cells/well), as it reduces cell culture and experimental cost, especially when expensive primary cells or other bespoke cellular models are required
- Automation compatibility means throughput can be scaled without excessive hands-on costs or time requirement
How do AI-enabled drug discovery teams actually generate the training data behind their models?
AI-enabled drug discovery companies increasingly rely on standardized, high-throughput perturbation assays, like Cell Painting and MERCURIUS™ DRUG-seq. Teams run these assays consistently across large-scale compound libraries to determine biological effects across cell types, dosages, and conditions, rather than relying on a patchwork of one-off experiments from different vendors, protocols, or publicly available data. This approach feeds a growing, comparable dataset back into models on a regular cadence. Consistency across datasets is crucial as small variations could train foundational models on noise instead of on true biological signal.
How long does it take to build an AI data moat with transcriptomic data?
It largely depends on throughput and assay size. With MERCURIUS™ 1536 DRUG-seq, users can include 1536 samples per run with ~2.5 hours of hands-on time, so they can move from thousands to tens of thousands of standardized transcriptomic profiles across ongoing AI-scale screening campaigns. For instance, at a throughput of 100 MERCURIUS™ 1536 DRUG-seq plates per month, AI-native drug discovery teams can generate one million perturbation profiles in approximately 1.5 months versus 6 months for standard MERCURIUS™ DRUG-seq in 384-well plates. This means the AI data moat builds continuously, as a byproduct of screens you’re already running, rather than requiring a dedicated data-generation initiative.
Does this replace or complement morphology-based screening like Cell Painting?
Complements. Imaging captures morphological and phenotypic change at scale while transcriptomics explains the underlying gene-regulatory mechanism behind it. Run alongside existing HTS/imaging pipelines, MERCURIUS™ 1536 DRUG-seq adds the molecular layer needed for mechanism-of-action clustering and toxicity-pathway detection that morphology alone can’t resolve.
Read the technical validation
Full technical performance data, including demultiplexing rates, gene detection saturation curves across five cell types, and plate-wide uniformity, is available in the MERCURIUS™ 1536 DRUG-seq tech note.
