By 2025, we will be able to design full antibodies and enzymes through advanced AI models, revolutionizing drug development.

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.

In the future, the pharmaceutical industry will integrate AI into drug design, making it an essential tool for biology and chemistry.

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.

In the next few years, we aim to build a comprehensive database of virtual cells to enhance drug target design and therapeutic success rates.

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.

As AI-designed drugs enter clinical phases, we must rethink how we engage with regulatory bodies and streamline the clinical trial process.

We aim to create a general drug design engine with AI that can be applied across various disease areas, not just targeting a single indication.

As we automate scientists and innovators, we may see rapid advancements in scientific research, but new bottlenecks will likely arise.