Cheminformatics and machine learning

Algorithmic advancements

Partnering with us means leveraging advanced technologies to accelerate your drug discovery process. Here's how we can support your projects:

• Predictive ML models: We develop and apply ML models to predict various physicochemical and ADMET endpoints, using data that is client-specific, public or via our life science database. These models help characterise and select the best hits from experiments, supporting hit-to-lead optimisation and reducing cycle times. 
• Quantitative SAR models: We utilise quantitative structure-activity relationship (SAR) models for data analysis, identifying activity cliffs and guiding compound optimisation by recommending the next compounds to synthesise based on available SAR data. 
• De novo generative chemistry: We deploy generative chemistry models to design novel molecules with optimised properties. Our multiparameter optimisation (MPO) strategy suggests innovative chemical structures to meet your desired profile. 

This approach can accelerate discovery while helping ensure precision and efficiency throughout the process.