Rahko is solving drug discovery with quantum machine learning
The long and slow path to discovering safe, effective drugs
The path to discovering and developing safe, effective drugs is slow and expensive, with a high rate of drug candidate attrition, and frequent, costly failure in clinical trials.
The drug discovery pipeline is still dominated by lab-performed experiments, and manually integrated processes. The space of potential chemical compounds is incredibly large, making screening through vast numbers of drug candidates an insurmountable process that is not attempted comprehensively due to the prohibitive cost and the time it would take to screen such a large space.
While computational methods are faster and cheaper compared to lab-performed experiments, they still lack precision and speed, both required to screen and reliably identify promising drug candidates from the enormous space of compounds.
Understanding the behaviour of drugs with physics-based and data driven approaches
Predicting the behaviour of drugs is a complex problem that typically relies on two components: physics-based simulations of chemical and biological processes, and data-driven predictions of interactions in the human body.
The speed and accuracy of physics-based simulations is limited due to our ability to simulate quantum mechanics. Data-driven predictions are limited by the scarcity of large, high-quality datasets.
Removing key bottlenecks in drug discovery
Rahko brings together three key technologies in its quantum discovery platform Hyrax: computational chemistry, machine learning and quantum computing.
The quantum machine learning models in Hyrax combine physics and machine learning, enabling speedups of chemical simulations, systematic limitation of prediction errors and reduction of the amount of data required.
This allows Hyrax to overcome the critical barriers in drug discovery of speed and prediction accuracy.
Hyrax – Rahko’s quantum drug discovery platform
Hyrax is a quantum drug discovery platform that is rooted in quantum mechanics.
Hyrax offers AI (quantum-based machine learning) for drug discovery that allows us to accelerate and slash costs of drug discovery today, and create a clear pathway to the game-changing prediction accuracy of the quantum computers of tomorrow.
State-of-the-art with AI today, quantum computing tomorrow
Hyrax brings together quantum-based machine learning and quantum machine learning descriptors and models for best-in-class lead identification and optimization of drug candidates.
Quantum-based machine learning drastically improves the speed at which we can screen drug candidates.
Quantum machine learning with near-term quantum computers will allow us to predict the properties of these candidates with vastly greater accuracy, and much less data.
Hyrax is designed to seamlessly integrate with current high performance computers, and multiple leading quantum computers including a range of QPUs including devices based on superconducting qubits, trapped ions, Rydberg atoms and photonics.
Get in touch with the Rahko team here.