AI Drug Discovery Stalled by Lack of Trustworthy Biological Data

Billions of dollars are spent annually to apply artificial intelligence (AI) to life sciences and drug discovery.

However, we have yet to see a flood of new drugs and therapies on the market that can be attributed to AI, despite its tremendous potential to accelerate discovery.

The root cause of this productivity gap comes down to data.

Compared with the massive datasets used to train large generalist language models like ChatGPT and Claude, the data available for biological models is far smaller and less standardized.

To unleash the potential of AI in life sciences and drug discovery, biological datasets must be authenticated, validated, and supported by standardized metadata.