Blog
Using AI in Drug Development to Identify Metabolites & Harness Their Insights
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.
Using AI in Drug Development to Identify Metabolites & Harness Their Insights
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.
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Transcript: AI and Drug Development - Keeping it Real with Pharma
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.
Transcript: AI and Drug Development - Keeping it Real with Pharma
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.
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Crash Course to Using AI to Determine Dose Selection
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.
Crash Course to Using AI to Determine Dose Selection
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.
Using AI & MIDD to Decode Drug-drug Interactions
Let’s explore how AI-driven approaches to drug development can be used to decode DDIs, why it is important to detect them, as well as how to integrate AI into your strategy of detecting potential DDIs.
Using AI & MIDD to Decode Drug-drug Interactions
Let’s explore how AI-driven approaches to drug development can be used to decode DDIs, why it is important to detect them, as well as how to integrate AI into your strategy of detecting potential DDIs.
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Introducing AtlasGEN™ Novel Drug Designer
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
