Publications & Blog
Advancing the Use of Artificial Intelligence In Harmony with Drug Regulation
Recently the FDA released a publication, “Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products,” and requested comments on the framework outlined within it from subject matter experts in the industry. VeriSIM Life, which provides a computational drug development platform that uses AI and ML to streamline the drug development process, was eager to contribute our thoughts and feedback to the conversation.
Advancing the Use of Artificial Intelligence In Harmony with Drug Regulation
Recently the FDA released a publication, “Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products,” and requested comments on the framework outlined within it from subject matter experts in the industry. VeriSIM Life, which provides a computational drug development platform that uses AI and ML to streamline the drug development process, was eager to contribute our thoughts and feedback to the conversation.
An AI Approach to Generating MIDD Assets Across the Drug Development Continuum
Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70
An AI Approach to Generating MIDD Assets Across the Drug Development Continuum
Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70
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Does AI Hold the Potential to Reduce Reliance on NHPs in Drug Development? This National Academies of Science, Engineering & Medicine Report Concludes “Yes.”
Non-human primates (NHPs) still hold undeniable value for the pharmaceutical and biotechnology industries, but with ethical and practical issues posing increasing challenges, it is clear that we need a smarter approach to utilizing NHPs. For this reason and in response to a request from Congress, a committee of experts organized by the National Academies of Science, Engineering & Medicine recently released a report on the usage of NHPs in biomedical research. This report's conclusions serve as guidance for subsequent action by the NIH to address the current critical NHP shortage.
Does AI Hold the Potential to Reduce Reliance on NHPs in Drug Development? This National Academies of Science, Engineering & Medicine Report Concludes “Yes.”
Non-human primates (NHPs) still hold undeniable value for the pharmaceutical and biotechnology industries, but with ethical and practical issues posing increasing challenges, it is clear that we need a smarter approach to utilizing NHPs. For this reason and in response to a request from Congress, a committee of experts organized by the National Academies of Science, Engineering & Medicine recently released a report on the usage of NHPs in biomedical research. This report's conclusions serve as guidance for subsequent action by the NIH to address the current critical NHP shortage.
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What Is Explainable AI & How Can It Help in the Context of Drug Discovery?
XAI is emerging as a methodology that can help improve trust, confidence, and successful adoption of AI-driven approaches – especially in the context of drug discovery. Let’s take a closer look.
What Is Explainable AI & How Can It Help in the Context of Drug Discovery?
XAI is emerging as a methodology that can help improve trust, confidence, and successful adoption of AI-driven approaches – especially in the context of drug discovery. Let’s take a closer look.
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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.
