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Generative AI & Drug Development – What You Need To Know
Researchers are beginning to look at the utility of generative AI in drug development for specific ways this particular type of machine learning technology might help streamline, improve, or otherwise accelerate the development of new drugs. Here’s what you need to know about generative AI and drug development.
Generative AI & Drug Development – What You Need To Know
Researchers are beginning to look at the utility of generative AI in drug development for specific ways this particular type of machine learning technology might help streamline, improve, or otherwise accelerate the development of new drugs. Here’s what you need to know about generative AI and drug development.
Dose Optimization: How to Get it Right
At the heart of dose optimization is a mandate to safeguard the health of study participants. From unwanted side effects to reduced efficacy and toxicity, the pharmacological effect of administered doses for any potential drug candidate must always be carefully vetted for safety and efficacy. Let’s take a closer look at dose optimization – what it is and how to get it right.
Dose Optimization: How to Get it Right
At the heart of dose optimization is a mandate to safeguard the health of study participants. From unwanted side effects to reduced efficacy and toxicity, the pharmacological effect of administered doses for any potential drug candidate must always be carefully vetted for safety and efficacy. Let’s take a closer look at dose optimization – what it is and how to get it right.
Biomarker Discovery and Validation Explained
Biomarkers play a crucial role in drug development, helping measure the effects of investigational treatments in humans during clinical trials. Recently, advancements in biomarker discovery and validation methods, spurred on by innovations in instrumentation and in silico predictive tools , are improving diagnosis, prognosis and disease monitoring. Let’s take a closer look at biomarker discovery and validation.
Biomarker Discovery and Validation Explained
Biomarkers play a crucial role in drug development, helping measure the effects of investigational treatments in humans during clinical trials. Recently, advancements in biomarker discovery and validation methods, spurred on by innovations in instrumentation and in silico predictive tools , are improving diagnosis, prognosis and disease monitoring. Let’s take a closer look at biomarker discovery and validation.
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University of Toronto Study Confirms What VeriSIM Life Already Knows About the Potential of AI in Drug Development
This discussion around budding research into the benefits of AI and machine learning in drug discovery matters not only because it demonstrates that these topics aren’t going away anytime soon, but that it further validates the ability of AI to predict drug behavior accurately and reduce the time and cost of developing a new drug candidate.
University of Toronto Study Confirms What VeriSIM Life Already Knows About the Potential of AI in Drug Development
This discussion around budding research into the benefits of AI and machine learning in drug discovery matters not only because it demonstrates that these topics aren’t going away anytime soon, but that it further validates the ability of AI to predict drug behavior accurately and reduce the time and cost of developing a new drug candidate.
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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.
