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Cheminformatics and machine learning

Accelerating drug discovery with ML

Cheminformatics workflows, enhanced by machine learning (ML), are essential to advancing drug discovery at Nuvisan. We use these powerful tools to analyse complex biological and chemical data, accelerate development timelines and deliver innovative solutions. ML algorithms uncover patterns in vast datasets that are challenging, if not impossible, for humans to detect, enabling the selection and design of novel molecules with optimised properties. This data-driven approach accelerates the identification of promising drug candidates, enhancing the efficiency and precision of the discovery process.

illustration of connecting different datapoints to find emerging patterns

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 utilize quantitative structure-activity relationship (SAR) models for data analysis, identifying activity cliffs, and guiding compound optimization 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 optimization (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.