Research Articles
Verification vs Validation: A Strategic Guide for Biomedical Model Credibility
This article provides a comprehensive guide to model verification and validation (V&V), tailored for researchers and professionals in drug development and biomedical sciences.
Epistemic vs Aleatory Uncertainty: A Guide for Building Trustworthy Computational Models in Drug Discovery
This article provides a comprehensive guide for researchers and drug development professionals on managing epistemic and aleatory uncertainty in computational models.
Calibrating Confidence: A Framework for Credible Model Projections in Drug Development
This article provides a comprehensive framework for understanding, improving, and validating the credibility and confidence of model projections in drug development.
Establishing Credibility: A Comprehensive Guide to Computational Model V&V Standards in Biomedical Research
This article provides a comprehensive guide to Verification and Validation (V&V) standards for computational models, tailored for researchers and professionals in drug development and biomedical fields.
Navigating the FDA's AI Model Credibility Assessment Framework: A Guide for Drug Development Professionals
This article provides a comprehensive guide to the U.S.
Why Model Validation is the Non-Negotiable Foundation of Reliable Computational Science
This article provides a comprehensive overview of model validation's critical role in computational science, particularly for researchers and professionals in drug development and biomedical fields.
Building Confidence in Computational Models: A Fit-for-Purpose Framework for Biomedical Research
This article provides a comprehensive guide for researchers and drug development professionals on establishing confidence in computational models, from early discovery to clinical application.
Verification and Validation in Computational Modeling: A Guide for Credible Drug Development
This article provides a comprehensive guide to Verification, Validation, and Uncertainty Quantification (VVUQ) in computational modeling for biomedical research and drug development.
Generative AI vs. Active Learning: A Strategic Comparison for Accelerating Drug Discovery
This article provides a comprehensive comparison of Generative AI and Active Learning, two pivotal machine learning paradigms transforming pharmaceutical R&D.
Assessing Active Learning Model Generalization: Strategies and Benchmarks for Biomedical and Drug Development
This article provides a comprehensive framework for assessing the generalization capabilities of active learning (AL) models, a critical challenge for their reliable application in data-scarce domains like drug development.