Our approach
Discover new molecules with AI. Optimise existing regimens with evidence.
We pursue two complementary routes: artificial intelligence to accelerate the discovery of new candidate molecules, and evidence-based optimisation of combination regimens to bring clinical value to the bedside sooner.
Route one
AI-enabled anti-infective discovery platform
Data, algorithms and experiments connected into a continuously accelerating loop for discovering and optimising new anti-infective candidates against resistant bacteria and fungi.
- 01
Data integration
Public and proprietary anti-infective activity, toxicity and structural data are collected, cleaned and standardised into high-quality datasets for model training.
- 02
Computational design and screening
Generative and predictive models propose candidate molecules, which are evaluated and ranked across activity, selectivity, stability and safety.
- 03
Experimental validation
Candidates are tested for in vitro activity, effect on resistant strains and biofilms, cellular selectivity and stability, and advanced to in vivo models where warranted.
- 04
Optimisation and feedback
Structures are optimised for drug-likeness, and every round of results flows back into the datasets and models so the design–validate cycle keeps accelerating.
What we design for
- New modes of action: we prioritise mechanisms that differ from existing drugs, to address established resistance.
- Activity against resistant organisms and biofilms: clinical resistant isolates and biofilm models are key benchmarks.
- Safety margin: selectivity and toxicity prediction enter the design early so high-risk molecules are excluded sooner.
- Manufacturability: feasibility and cost of synthesis or expression are considered so candidates can become products.
Route two
Combination and regimen optimisation of approved drugs
Building on the established safety of approved drugs, we use mechanistic synergy and pharmacokinetic/pharmacodynamic evidence to optimise combinations, formulations and dosing strategies so valuable regimens reach patients faster and in a standardised way.
Greater efficacy
Complementary mechanisms act synergistically to reduce pathogen burden faster and improve response in difficult-to-treat infections.
Lower toxicity
Combination therapy can reduce the dose or exposure required of each agent, lowering the risk of liver and kidney toxicity and other adverse effects.
Delayed resistance
Multi-target synergy reduces the selective pressure for escape mutations at any single target.
Better adherence
Simplified regimens and formulations reduce medication errors and make standardised use and real-world evidence collection easier.
Why this route
Clinical practice already relies on many empirical combinations, but they often lack systematic mechanistic study, dose optimisation and standardised protocols. Approved drugs come with well-characterised safety and pharmacokinetic data, which makes validation of a combination regimen shorter and more predictable, and makes it easier to translate into guideline-recommended, reimbursable standard care.
How we work
How we advance our research
Clinically driven
We define problems together with front-line teams in infectious disease, critical care, haematology and transplantation, starting from real treatment dilemmas.
Evidence based
We follow the evidence ladder from in vitro to in vivo to clinical studies, with explicit decision points at each stage; data decide whether a programme continues.
Compliant and ethical
We adhere to pharmaceutical regulation, GLP/GCP standards and ethical requirements, protecting research participants and data.
Open to collaboration
We work in an asset-light model with research institutions, contract research organisations and industry partners, and welcome joint research, co-development and licensing.