Innovate

A new paper, “Data-driven federated learning in drug discovery with knowledge distillation”, was recently published in Nature Machine Intelligence, the leading journal for Machine Learning and AI. It explores the potential for federated learning to advance drug discovery through secure, collaborative research, representing a major step forward in privacy-preserving machine learning.   What is Federated

At Lhasa we know that there are many challenges involved in carrying out risk assessments for drug substances and drug products, to ultimately ensure a safe and effective product for patients. Forced degradation studies are essential in pharmaceutical development to assess drug stability and ensure product quality and safety. These studies help identify degradation pathways,

The use of in silico prediction systems has become an essential part of regulatory submissions when assessing the genotoxic potential of chemicals. Guidelines such as those from the EMA, US FDA, and ICH highlight their importance. Most notably, the ICH M7 guidance explicitly requires the application of two complementary quantitative/qualitative structure-activity relationship ((Q)SAR) systems to

Identification and control of impurities in active pharmaceutical ingredients (APIs) and pharmaceutical drug products is critical in drug development. Mirabilis is our innovative solution designed to address this challenge. In this post, we’ll answer ten frequently asked questions about Mirabilis.   What is Mirabilis? Mirabilis is an in silico tool developed by Lhasa Limited that

For over four decades, at Lhasa Limited we have been developing scientific software solutions that enable scientists to make informed decisions on the safety of drugs, chemicals and cosmetics. Our journey advanced significantly with the creation of Derek Nexus, an in silico expert knowledge-based toxicity prediction tool. As regulatory landscapes evolved and the push for