Derek NexusDerek Nexus is the expert, knowledge-based software that gives you accurate toxicity predictions quickly. Early, accurate in silico toxicity tests using Derek Nexus is the quick, inexpensive way to identify potentially toxic chemicals, aiding your experts in rejecting unsuitable drug candidates.MirabilisMirabilis is an industry-standardised approach providing an expert and scientifically robust software for the calculation of purge factors of potentially mutagenic impurities in a synthetic route.Sarah NexusSarah Nexus is a statistical software tool that gives you accurate mutagenicity predictions quickly.ViticVitic is the next generation chemical database and information management system, offering researchers and scientists rapid access to searchable toxicological information. Early review of Vitic is the quick, inexpensive way to identify potentially toxic chemicals, and therefore reject unsuitable drug candidates.
Vitic Nexus is your trusted toxicity expert and management system; containing data implemented by scientists at Lhasa Limited, who continually work on the toxicity database with current toxicological knowledge.ZenethZeneth is an expert, knowledge-based software that gives you accurate forced degradation predictions quickly. Zeneth is the perfect cost-effective solution for scientists who need to understand the forced degradation pathways of organic compounds.
Zeneth is your trusted degradation expert system. It is based on data implemented by scientists at Lhasa Limited, who continually work on the transformation knowledge base with current transformation knowledge.
This FAQs section details frequently asked questions about the ICH M7 guidelines. For FAQs on Lhasa products, please refer to the relevant product area.
Does Sarah Nexus give information on mitigating factors when a compound with a reactive group does not get an alert?
Yes, Sarah was designed to give interpretable predictions.
Predictions are derived from the self-organising hypothesis network. With this approach, the model organises the training data into hypotheses (clusters) based upon the presence of key structural fragments – which can be activating or deactivating. A typical prediction will be derived by considering several of these hypotheses that are matched to fragments of the query molecule. The signal from each of these hypotheses is shown – being generated by considering the training compounds held within that cluster. This then gives the user a high-level view to all the activating and deactivating fragments found by the model. So provides all the fragments that the model has learnt to be deactivating. For a more complete analysis, the user can look at the supporting compounds within each hypothesis which are ordered by relevance to the model. This then provides the user with the ability to challenge each step the model takes and use expert analysis to come to a judgement of the model’s trustworthiness in addition to the confidence metric that is provided alongside the overall call.