UL REACHAcross™

Meet the REACH 2018 registration deadline with smart, automated, and cost-effective read-across

With the final European REACH deadline quickly approaching in May 2018, companies are urgently gathering critical toxicity information about their products. But with only about 3% of the 83,000 chemicals used in U.S. commerce having been studied by regulatory bodies to assess human health impacts, finding the needed information can be expensive and time consuming.

UL REACHAcross™ is a ground-breaking digital tool that uses artificial intelligence and machine learning to advance science and meet your compliance needs. This digital assistant for REACH compliance offers the best of both worlds: the objective computational approach of a QSAR combined with the proven robustness and acceptability of a read-across system. This alternative delivers accurate information you need for compliance faster and more cost effectively than animal testing, consulting and other alternatives.

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HOW IT WORKS

REACHAcross™ is a highly advanced in silico tool whose machine-learning engine builds networks of chemicals based on molecular structure, while complex algorithms make half a billion calculations per assessment to find associations between chemicals and endpoints.

With one of the largest collections of chemical structures and endpoints (and growing), the database includes:
• 70 Million Structures
• 300,000 Endpoint Labels
• 250,000 High Interest Compounds
• 20,000 Unique Compounds with Endpoint Information

In four simple steps, REACHAcross™ delivers delivery of complete results for both existing and new chemicals.

1) Our sophisticated engine builds large networks of chemicals based on properties such as molecular structure and health endpoints interactions. The REACHAcross™ software system uses curated data from multiple, widely accepted public sources.
2) Using advanced machine-learning and complex mathematical algorithms, the system is able to accurately assess health impacts for the chemical entered with the certainty of an animal test and sensitivities of 80% across endpoints.

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