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VeriSIM Life recently announced the availability of AtlasGEN™ Novel Drug Designer, which is the first and only platform that combines generative chemical discovery with biological validation to compress drug candidate selection and reduce costly experimental research dramatically. AtlasGEN is different because it constrains the generative AI with biological data, ensuring relevant and accurate results. Instead of returning 1000 potential molecular hits, most of which are not good, AtlasGEN reduces wasted time, energy and money by returning only the results that are viable.

This article explores the significance of metabolites in drug development and how artificial intelligence (AI) is revolutionizing metabolite identification, leading to improved drug development outcomes. We will delve into the role of metabolites in drug development, the challenges of metabolite identification, and the benefits of AI-powered metabolite identification.

The following transcript was taken from a recorded conversation between Dr. Jo Varshney, CEO of VeriSIM Life, and Dr. Grazia Rovelli, Senior Researcher and Project Leader, Italfarmaco Group. Read on to understand how established companies are approaching AI and integrating it into their strategy as well as their research and development workflows.

The development of a drug is a complex and lengthy process, requiring precise decisions about dosing to ensure both the efficacy and safety of a drug. Artificial intelligence (AI) can be used for dose selection in drug development—helping to save time, money, and increase accuracy. Before embarking upon this journey, it is important to grasp not only the basics of dose selection and why it’s important in drug development, but also how exactly AI improves the dose selection process.