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AI in drug discovery — Europe risks watching from the sidelines as US and China accelerate

Since 1950, the cost of developing a new drug has doubled every nine years — Eroom's Law. Today, bringing a molecule to market takes 10–15 years and over $2 billion. AI promises to close the loop: ana...

Ing. Calogero Bono 2 min read
AI in drug discovery — Europe risks watching from the sidelines as US and China accelerate

Since 1950, the cost of developing a new drug has doubled every nine years — Eroom's Law. Today, bringing a molecule to market takes 10–15 years and over $2 billion. AI promises to close the loop: analyze clinical, genomic and lab data faster, predict effective compounds. But the raw material — data — sits fragmented inside corporate silos and under divergent regulations. Those who own and process it win. And right now, that's the US and China.

An in-depth report by MIT Technology Review shows that the “data loop” is the key: more data → better models → faster trials → more data. A virtuous cycle that requires infrastructure, investment and agile regulation to sustain.

Why it matters — for Europe and Italy

Europe has world-class basic research: Italy's Menarini, Germany's BioNTech, France's Sanofi. But when it comes to AI-driven drug discovery, the landscape shifts. Health data across EU members is regulated by GDPR, yet each member state interprets it differently. Aggregating datasets for large-scale model training is a regulatory obstacle course. Meanwhile, the US pushes the NIH All of Us initiative, and China runs a national precision medicine program with centralized data.

For an Italian pharma company or a biotech SME, the choice is stark: either partner with a US big tech (paying in data and royalties) or build proprietary infrastructure at prohibitive cost. The result? Europe risks becoming a customer of others' discoveries, losing strategic control over therapies and pricing.

Our stance

Noi, di Meteora Web, see this every day in the projects we follow: the digital divide isn't just about websites or e-commerce — it's about the ability to use data for innovation. Supporting open-source and controlled data sharing isn't ideology: it's economic pragmatism. Europe must invest in sovereign AI platforms for drug discovery — anonymized datasets, transparent models, equitable access. If it doesn't, the gap won't close on its own. Data security matters, but paralysis is far more dangerous.

What to do — concrete actions

For those in Italian pharma biotech: join European consortia like the European Health Data Space. Push for common interoperability standards. Invest in open-source AI models (e.g. MolPAL, DeepChem) to avoid vendor lock-in with American firms. And ask your government for tax incentives for shared research. The era of silos is over: those who don't share data today will have no drug discovery tomorrow.

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