Publications & Blog
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Enhancing FDA-Approved AI/ML Methods: VeriSIM Life's Comprehensive Approach to Drug Development and Patient Selection
The FDA's development and use of an AI/ML-based scoring rule for patient selection in the Emergency Use Authorization (EUA) of anakinra to treat COVID-19 marks a significant milestone in the integration of advanced technologies in drug development and patient care. This article explores the FDA's approach, its requirements, and how VeriSIM Life's cutting-edge techniques offer an even more informative and powerful methodology for drug development and patient selection.
Enhancing FDA-Approved AI/ML Methods: VeriSIM Life's Comprehensive Approach to Drug Development and Patient Selection
The FDA's development and use of an AI/ML-based scoring rule for patient selection in the Emergency Use Authorization (EUA) of anakinra to treat COVID-19 marks a significant milestone in the integration of advanced technologies in drug development and patient care. This article explores the FDA's approach, its requirements, and how VeriSIM Life's cutting-edge techniques offer an even more informative and powerful methodology for drug development and patient selection.
How AI Assists Small Molecule Drug Design and Development
From identifying novel drug targets to optimizing the manufacturing process, AI is transforming the way small molecule drugs are discovered, designed, and developed. AI-driven drug development holds remarkable potential for accelerating the availability of life-changing therapies through small molecule drug development. Read on to connect the linkages between small molecule drug chemistry, the drug discovery and development process, and the application of AI to deliver safe and effective drugs for clinical use in patients.
How AI Assists Small Molecule Drug Design and Development
From identifying novel drug targets to optimizing the manufacturing process, AI is transforming the way small molecule drugs are discovered, designed, and developed. AI-driven drug development holds remarkable potential for accelerating the availability of life-changing therapies through small molecule drug development. Read on to connect the linkages between small molecule drug chemistry, the drug discovery and development process, and the application of AI to deliver safe and effective drugs for clinical use in patients.
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Supercharging QSP with a Comprehensive View of the Drug Development Process
In the ever-evolving landscape of drug development, scientists are continually seeking innovative approaches to streamline processes, reduce costs, and enhance efficiency. One such approach is Quantitative Systems Pharmacology (QSP) modeling, a multidisciplinary field that integrates mathematics, biology, and pharmacology to facilitate a deeper understanding of the complexities underlying disease and drug response. With the growing prominence of Artificial Intelligence (AI), QSP modeling is poised for transformative advancements that could revolutionize the drug development pipeline.
Supercharging QSP with a Comprehensive View of the Drug Development Process
In the ever-evolving landscape of drug development, scientists are continually seeking innovative approaches to streamline processes, reduce costs, and enhance efficiency. One such approach is Quantitative Systems Pharmacology (QSP) modeling, a multidisciplinary field that integrates mathematics, biology, and pharmacology to facilitate a deeper understanding of the complexities underlying disease and drug response. With the growing prominence of Artificial Intelligence (AI), QSP modeling is poised for transformative advancements that could revolutionize the drug development pipeline.
Overcoming Challenges in Transdermal Formulations: How AI Improves Patient Experience
VeriSIM Life’s BIOiSIM® platform can address the challenges of TD formulation development because its hybrid AI models capture the complex interplay between the API, excipients, and physiology. BIOiSIM uses a unique combination of mathematical and AI-driven techniques to analyze and predict a drug’s behavior in the human body. The platform's models provide deep insight into drug distribution in various skin layers and underlying tissues, such as joint synovial membranes, and capture a drug’s systemic exposure and clearance post-transdermal application.
Overcoming Challenges in Transdermal Formulations: How AI Improves Patient Experience
VeriSIM Life’s BIOiSIM® platform can address the challenges of TD formulation development because its hybrid AI models capture the complex interplay between the API, excipients, and physiology. BIOiSIM uses a unique combination of mathematical and AI-driven techniques to analyze and predict a drug’s behavior in the human body. The platform's models provide deep insight into drug distribution in various skin layers and underlying tissues, such as joint synovial membranes, and capture a drug’s systemic exposure and clearance post-transdermal application.
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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.
