There's a significant opportunity in generating in vivo data, which is currently limited, to better understand drug interactions in real biological systems, and we're seeing breakthroughs in data-generating technology in biology and chemistry that will significantly impact how we model these fields.

Synthetic data can be used in training models, but it must be created from the same distribution to maintain quality.

Synthetic data is generated from broad categories of user requests, allowing the model to explore specific harmful examples and improve its classifiers.

The promise of AI in drug discovery lies not just in predictive power but in the ability to simulate experiments in a parallelized fashion, which could significantly reduce the time and cost involved in traditional methods.

To create synthetic data sets for complex systems, we need a comprehensive catalog of components that is significantly larger than the typical system design, potentially requiring hundreds of millions of parts.

Generative models are essential for navigating the vast molecular space, allowing us to explore potential designs that could lead to effective therapeutics.

Quantum-enabled discovery could revolutionize fields like drug discovery by allowing quantum computers to predict molecular behaviors, significantly speeding up the process of synthesizing complex molecules without relying on trial and error.

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

Building a diverse team of experts in AI, chemistry, and biology is essential for questioning traditional approaches and fostering innovation in drug design.