We have over twenty years of experience in data processing, data-driven modeling, and building software products that utilize machine learning.
We apply machine-learning to chemistry. Our products maximize the utility of data, reducing the time and cost of chemical R&D.
We build enterprise solutions. Designed with integration in mind, our technologies are both accessible via API and deployable on our partners’ infrastructure.
ToxTrack Co-Founder, Tom Luechtefeld, published a series of articles exploring the public data set released by the European Chemical Agency. The research focused on exploiting the large volume of data to inform predictive models.
Seeing the need for accurate predictive toxicological tools, UL partnered with ToxTrack to turn Tom’s research into fully-fledged products that reduce the need for animal testing and help companies comply with global regulations.
The formal product launch of UL REACHAcross -- a groundbreaking tool for predictive toxicology. UL REACHAcross uses advanced machine learning techniques, trained one of the largest chemical data sets, to inform its predictions.
ToxTrack continues to develop novel tools and technologies. We are always looking for new opportunities to collaborate on projects within and outside the toxicological and machine-learning communities.
Toxicological Sciences Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility (Tom Luechtefeld, Dan Marsh)
NIH, National Toxicology Program Workshop: Predictive Models for Acute Oral Systemic Toxicity
Founder & Lead Scientist
Chief Technology Officer
Chief Revenue Officer