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Artificial Intelligence (AI) has become a powerful force within the pharmaceutical industry, catalysed by the challenging lengthy and costly drug development process. To maximize the impact of AI, it is critical to have access to enough good quality data to allow machine learning algorithms to extract relevant knowledge and produce valuable models.

In his blog “Human review of in silico predictions of toxicity”, Principal Scientist, Alex Cayley, outlined the reasons why expert review is essential when using in silico tools to assess toxicity, particularly in the ICH M7 use case where expert review of two complementary in silico systems is required [1].

In this post we discuss how consideration of the user’s journey led to Lhasa tackling the ICH M7 use case with a brand-new automated workflow, now available to Derek and Sarah users in Nexus 2.3. I’ll also show some worked examples of results from the new system.

Coronaviruses are a large family of viruses which can affect humans or other species and can range anywhere from a mild illness such as the common cold to more severe illnesses such as Severe Acute Respiratory Syndrome (SARS). Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).