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Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Discovering Closed-Form Pharmacokinetic Relationships with Symbolic Regression to Improve Interpretability, Mechanistic Plausibility, and Extrapolation Beyond Black-Box Prediction
inferred← citeshttps://doi.org/10.51847/ytsrgcqu2zopenalex-snapshot
Mechanistic Plausibility before Predictive Performance: Reordering the Standards by Which Computational Pharmacology Models Are Judged
inferred← citeshttps://doi.org/10.5599/admet.2772openalex-snapshot
Leveraging machine learning models in evaluating ADMET properties for drug discovery and development
inferred← citeshttps://doi.org/10.68159/c710578243openalex-snapshot
Explainable AI-Generated Formulation Designs for Regulatory and Scientific Decision-Making
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