EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·Jul 27·5 min read
Closing the data loop in AI-driven drug discovery
TL;DR
AI-driven drug discovery is evolving to close the data loop, significantly reducing the time and cost of bringing new drugs to market. This approach leverages real-time data feedback to enhance decision-making in the drug development process.
✦ Why It Matters
Explore AI tools that facilitate real-time data analysis in your drug discovery projects today.
Key Takeaways
- Incorporate diverse datasets to improve AI model training.
- Utilize tools like Cytiva’s Image Integrity Checker for data verification.
- Focus on integrating lab systems for better data flow.
- Stay informed about regulatory changes affecting AI in drug discovery.
How It Works
AI in drug discovery shifts from traditional empirical screening to predictive design, allowing researchers to create drug candidates based on predicted interactions with disease targets. This method increases the number of potential hits and improves their quality, enabling earlier elimination of low-quality candidates.
⚠ The Catch
AI models struggle with predicting the kinetics and developability of new compounds, necessitating lab validation for every AI-generated candidate.