Science

AI and LLMs are rapidly shifting how we work, and science is no different. 🧬

The conversation around drug discovery is particularly intriguing - and urgent. With drug development costs of $2.6B and a 90% failure rate, pharma needs AI more than ever.

But is AI living up to its promise? 🧵

#DeSci biopharma IP tokenization protocol.


Fund research & shape early-stage research ideas on http://catalyst.molecule.xyz

Let's start with what AI can do for drug discovery. 💊

1. Identify patterns in vast datasets
3. Predict drug-target interactions
4. Prioritize promising lead compounds
5. Design efficient clinical trials
6. Identify new uses for existing drugs

And that's just the beginning

A ton of questions are still on the table.

❓Who ultimately owns and benefits from the IP?
❓How do we fund diverse approaches?
❓Won't we need more access to data? 
❓Will the industry become more collaborative or siloed?

That's what DeSci is for 🧬

With DeSci, the future of AI drug discovery will be:

🌐 Collaborative & open
🤝 Community-funded
🔎 Transparent 
💨 Accelerated
👍 Capital efficient

But we have to start somewhere 👇

Molecule is tackling some of the biggest bottlenecks in drug development with: 

✨ Novel Funding Mechanisms
🌐 Decentralized IP 
🦠 bioDAOs for collaborative research

That is why we've built products like @pumpdotscience and Catalyst—to reshape how research is funded & conducted

We aren't alone in envisioning a decentralized future.

🦠 bioDAOs offer significant opportunities for pharma to discover and fund innovative research projects.

Just like Pfizer did by supporting @vitadao

The future of pharma isn't just AI - it's decentralized, community-owned, and happening in DeSci! 

Let's accelerate science together!

Big Pharma is going all-in on AI partnerships:

OpenAI building with Moderna, Sanofi, Lilly, & more
AstraZeneca partnering with BenevolentAI
Aitiabio building digital twins with Roche, Merck & more

Yet these siloed approaches might be missing the bigger picture - the potential of decentralized, collaborative science.