AI for Scientific Discovery

Principles and workflows for building AI systems that accelerate scientific research, derived from the development of AlphaFold at DeepMind.

Core Principles

Research Ideas as Multipliers

Research and architectural innovation multiply the value of data and compute rather than adding linearly.

Key insight: AlphaFold2 trained on 1% of available data outperformed AlphaFold1 trained on 100% of data. This demonstrates that ideas can provide 100x leverage over data alone.

Implication: When building ML systems for scientific domains, prioritize research iteration over data acquisition. Most compute budget should account for failed research iterations, not final model training.

Many Mid-Scale Ideas

Breakthroughs come from accumulating many medium-sized innovations, not one revolutionary idea.

Key insight: In AlphaFold ablation studies, no single component explained more than 10% of the system's improvement. The breakthrough emerged from combining numerous architectural innovations.