Blog
Mechanistic Models in Theory and in Practice
Mechanistic models serve as a useful candidate for modeling the detailed inner workings of a system in a true-to-life manner, going above and beyond simulating basic input and output. They can be used as a tool to improve preclinical translatability, reduce time and cost investments, and solve real-world problems safely.
Mechanistic Models in Theory and in Practice
Mechanistic models serve as a useful candidate for modeling the detailed inner workings of a system in a true-to-life manner, going above and beyond simulating basic input and output. They can be used as a tool to improve preclinical translatability, reduce time and cost investments, and solve real-world problems safely.
When It Comes to AI in Drug Development, Should We Believe the Hype?
AI technologies like deep learning, machine learning and natural language processing have the potential to address many of the challenges that traditionally plague drug R&D – accelerating molecule design and testing, streamlining essential processes, improving chances of clinical success, and reducing costs throughout the development pipeline.
When It Comes to AI in Drug Development, Should We Believe the Hype?
AI technologies like deep learning, machine learning and natural language processing have the potential to address many of the challenges that traditionally plague drug R&D – accelerating molecule design and testing, streamlining essential processes, improving chances of clinical success, and reducing costs throughout the development pipeline.
Accelerating novel drugs to patients together–how combining discovery and development technologies will transform drug decision making
BIOiSIM is a first-in-class decision-making engine that can take your toughest questions and generate answers to guide your next steps at any stage of drug development. With the acquisition of Molomics, it will be powered by one of the largest and fastest growing inferential search spaces in existence, encompassing over 1 trillion compounds.
Accelerating novel drugs to patients together–how combining discovery and development technologies will transform drug decision making
BIOiSIM is a first-in-class decision-making engine that can take your toughest questions and generate answers to guide your next steps at any stage of drug development. With the acquisition of Molomics, it will be powered by one of the largest and fastest growing inferential search spaces in existence, encompassing over 1 trillion compounds.
Patient Stratification: What You Need to Know to Do it Right
In today’s complex world, we truly only understand a tiny fraction of biology. When performing clinical trials, scientists/researchers must figure out ways to develop methods to effectively represent a whole with a subset or sampling due to costs and time. These methodologies, when done correctly, are all based on statistical methods. This is true for population genetics and patient stratification for clinical trials and studies.
Patient Stratification: What You Need to Know to Do it Right
In today’s complex world, we truly only understand a tiny fraction of biology. When performing clinical trials, scientists/researchers must figure out ways to develop methods to effectively represent a whole with a subset or sampling due to costs and time. These methodologies, when done correctly, are all based on statistical methods. This is true for population genetics and patient stratification for clinical trials and studies.
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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.
