Our core technologies

Screening library design, structure-activity relationships, docking simulations across fragments, small molecule drugs, metabolites and oligonucleotides.

Extensive experience in multi-omic analyses, from DNA through RNA to proteomes and metabolomes. Protein structure and function analyses by AI and molecular simulation.

Mechanistic and statistical (i.e. hybrid) modelling of biochemical reaction networks up to whole manufacturing processes, including Process Analytics and Data Management.

From basic Pattern Recognition through Machine Learning up to Deep Learning for Predictive applications across drug and sensor design, protein structure analysis, and cell image analysis.

While AI has had a transformative effect in fields such as mass spectrometry, many other analytical techniques have been overlooked.  We have driven chemometric advances in electrochemical, calorimetric and other spectroscopic data. 

Utilising genetic algorithms, statistical modelling and Machine Learning, our approaches have been successful in the design and optimisation of novel diagnostics, metabolic engineering, protein engineering, and cell culture media.