Use Neural Designer to model complex natural systems, discover relationships in experimental data, and turn scientific measurements into predictive models.
Where Neural Designer applies
Natural and life science applications often combine measured variables, laboratory results, environmental observations, and domain knowledge to predict properties, classify patterns, or detect abnormal behavior.
Neural Designer supports workflows in physics, chemistry, biology, environmental science, materials science, biotechnology, agriculture, forestry, and food science.
How machine learning helps
Build models from experimental, laboratory, sensor, or observational data.
Estimate target properties, classify samples, detect risks, and support scientific decision-making.
Representative use cases
QSAR and chemical modelling
Relate molecular descriptors or chemical structure to properties and biological or environmental behavior. Read use case ›
Gas emissions reduction
Model emissions and identify operating conditions that reduce environmental impact. Read use case ›
Virtual sensing
Estimate physical measurements from related variables when direct sensing is costly or difficult. Read use case ›
Microarray data analysis
Analyze high-dimensional biological data to identify patterns in genes and samples. Read use case ›