the platform.

Our platform leverages the convergence of artificial intelligence (AI), computational chemistry, and automated workflows.

At its core, the technology employs machine learning algorithms that are able to autonomously direct computational simulations and learn from the outcomes to improve themselves.

These algorithms learn complex relationships between molecular features and desired outcomes, enabling in silico prediction of compound efficacy and properties.

Computational chemistry methods, including molecular dynamics, quantum mechanical and docking calculations are integrated to refine predictions and assess compound viability and interactions with biological targets.

The platform then automates the design of novel compounds based on these predictions, generating virtual libraries of potential candidates.

These virtual compounds are prioritized based on predicted performance. The platform operates automatically via workflows – ‘pipelines’ – that connect various computational tools for iteratively producing better and better virtual libraries of candidate molecules.

Our Process

1. Understanding your challenge

Our experts delve into your unique needs, working with you to grasp the intricacies of your business and address your challenges with precision and care.

2. Pilot study

Next, we design and run an initial project to ensure the simulations align with real-world data. This step is crucial for us to explore various design possibilities, ensuring we’re on the right track.

3. Simulation and optimisation

Our integrated AI and simulation software repeatedly modifies the parameters, learning what’s needed to create your optimal product.

4. Lab verification

Predicted products can finally be made and rigorously tested in a lab environment. All results are fed back into the simulations for further optimisations if necessary.

Double helix