Publications
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Knowledge-enhanced AI to Supercharge ADC Development for Treatment of Cancer
The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.
Knowledge-enhanced AI to Supercharge ADC Development for Treatment of Cancer
The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.
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50 shades of AI in regulatory science with Dr. Weida Tong from FDA
Understanding the context-of-use for each AI shade is crucial to address biases, ensure transparency, and enhance decision-making processes within regulatory frameworks. In this article, the authors emphasize the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.
50 shades of AI in regulatory science with Dr. Weida Tong from FDA
Understanding the context-of-use for each AI shade is crucial to address biases, ensure transparency, and enhance decision-making processes within regulatory frameworks. In this article, the authors emphasize the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.
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
Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients
In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients.
Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients
In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients.
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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.
