e-book: What type of data do you need for AI Drug Discovery?
A practical guide to overcoming the data preparation bottleneck, understanding which data types matter, where they fall short, and how to build datasets that AI can actually use.
14 pages · Based on peer-reviewed research
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The data problem is bigger than most teams realize
The pharmaceutical industry doesn’t lack data, it lacks usable data. Most AI initiatives stall not because of algorithmic limitations, but because the datasets feeding those models were never properly prepared in the first place.
95%
of AI projects fail to deliver on their promises, primarily due to poor data quality, not algorithmic shortcomings (MIT, 2025)
$6.16B
Average estimated cost to develop a new drug, with preclinical research accounting for more than 43% of total spend
80%
of all healthcare data exists in unstructured formats: clinician notes, discharge summaries, PDFs, that AI cannot directly consume
Practical knowledge, not theoretical promises
This guide draws on published research, real-world case studies, and current regulatory guidance to give you a grounded view of the data landscape in AI-driven R&D.
The five core data types in drug discovery
From molecular representations to clinical records, what each contributes and where it breaks down.
Multimodal data and where it's heading
Cell Painting assays, single-cell RNA-seq, spatial transcriptomics, CRISPR screening, and clinical trial data lakes, a clear-eyed look at emerging modalities and the trade-offs they carry.
Why volume is not the same as value
Models trained on poor data pass benchmarks and fail in practice.
The AI data preparation bottleneck, mapped
The exact stages where data preparation breaks down, and what’s behind each failure point.
Even the most advanced algorithms become useless if they are trained on benchmark data that is not adapted to the real problems of drug discovery. These models may perform well in retrospective tests but fail in real-world applications.
— From the ebook, on the gap between benchmark performance and prospective validation