Precision Design

Drug design is engineering at the molecular scale.

Every position of every atom and every bond determines the future success of a molecule.

Truly using artificial intelligence to undertake the design process allows us to be far more efficient and allows us to build new end to end solutions that we believe will fundamentally redefine the process of drug creation.

Working with sparse data allows us to design potential first-in-class molecules for the latest high-interest targets.

Generative design and active learning deliver unprecedented efficiencies to candidate discovery.

Balanced molecules encode efficacy, safety and bioavailability and maximise the chance of success in a clinical setting.

Data Agnostic

For each project we collect all relevant experimental information irrespective of data type.

Design & Learning

Our systems transform drug discovery into effective formalised design moves.

Exploration & Exploitation

Our systems design molecules that simultaneously address multiple clinical requirements.

Through a combination of AI design, predictive models and experiment, our systems quickly explore and learn which areas of chemistry are most likely to balance the complex requirements for each drug discovery project.

Once promising areas are identified, the systems focus on designs that exploit those areas in order to identify the best compounds.

This balance of early exploration followed by exploitation enables rapid learning and delivers unparalleled progress from initial hits to clinical candidate.

Generative Design

Designing molecules that balance all project requirements of efficacy, selectivity, safety and bioavailability is a significant task.

Generative design can operate in two and three dimensions. In the video, we used a 3D example to visually demonstrate evolution within a protein structure binding site.

Millions of compounds are evolved during each design cycle and from these we synthesise and test key compounds of highest potential interest. Experimental data not only gives compound specific feedback but is used to assess and refine the predictive power of the model platform.

As a project progresses, the system moves from an exploration phase to an exploitation strategy. By now, molecules are consistently fulfilling key project goals and from these we select a candidate molecule suitable for preclinical testing.