Neural networks trained on quantum physics are changing molecular science

The fusion of decentralized AI and molecular simulations is creating a breakthrough approach to drug discovery that could dramatically accelerate the development of new treatments. By generating high-fidelity simulations at the atomic level, researchers can predict molecular interactions with unprecedented speed and accuracy, potentially compressing years of laboratory work into days of computational modeling—all at a fraction of the traditional cost.

The big picture: Rowan Labs has released Egret-1, an open-source suite of neural network potentials that can simulate organic chemistry with atomic precision at speeds up to a million times faster than conventional supercomputers.

• The technology trains AI not on internet data but on quantum mechanics equations, creating synthetic datasets that mirror real-world physics at the molecular level.
• By partnering with Bittensor’s decentralized AI protocol Macrocosmos (subnet 25), Rowan has added distributed computing power to their high-accuracy simulations.

How it works: The system trains neural networks to replicate the outputs of quantum mechanics equations, essentially creating a specialized AI that “thinks” in molecules rather than text or images.

• “We’re training neural networks to recreate the outputs of those equations. It’s like Unreal Engine, but for simulating the atomic-level real world,” explained Ari Wagen, Rowan Labs Co-founder.
• The technology can predict crucial pharmacological properties, such as how tightly potential drug compounds bind to proteins, without running physical laboratory experiments.

Why this matters: This approach could fundamentally transform multiple industries beyond pharmaceuticals, with applications already in development for environmental remediation and advanced materials.

• The combination of atomic-precision simulations and decentralized computing could compress the traditional drug discovery timeline by years, potentially leading to faster treatments for rare diseases.
• Applications extend to carbon capture technology, atomic-level manufacturing, and environmental cleanup solutions like oil spill remediation.

What they’re saying: Rowan’s founders envision building increasingly powerful models that can fundamentally change how we approach scientific discovery.

• “Instead of running experiments, you can run simulations in the computer. You save so much time, so much money and you get better results,” said Wagen.
• “We can predict how fast materials break down, or optimize catalysts to degrade pollutants,” explained Jonathon Vandezande, Rowan’s Co-founder and materials scientist.