QSAR Lab

QSAR Lab is a research spin-off working at the intersection of computational chemistry, toxicology, data science, and regulatory support.

The company develops in silico New Approach Methodologies (NAMs) for chemical and material safety assessment, supporting more predictive, data-driven, and animal-free research. Core expertise includes QSAR and read-across models, physicochemical and toxicological property prediction, predictive software, digital platforms, and AI- and machine-learning solutions for chemicals, pharmaceuticals, and advanced materials.

With over 20 years of experience, QSAR Lab delivers regulatory-oriented solutions supporting research, product development, safety assessment, and informed decision-making across industry, regulatory contexts, and collaborative research projects at every stage of innovation.

Contribution to EmerGO

QSAR Lab will apply machine learning methods to predict the properties, activities, and environmental behaviour of persistent and emerging pollutants. The team will also contribute to an open-access database integrating QSPR/QSAR predictions with monitoring and experimental data, including models supporting the prediction of pollutant elimination and reduction.