Summary of Discovering Intrinsic Multi-compartment Pharmacometric Models Using Physics Informed Neural Networks, by Imran Nasim et al.
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
by Imran Nasim, Adam Nasim
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel data-driven pharmacokinetic-informed neural network model is introduced, which efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures, reliably forecasting their derivatives. The resulting models are interpretable and explainable through Symbolic Regression methods. This framework demonstrates the potential to significantly enhance model-informed drug discovery. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Pharmacometric models help develop new medicines. Scientists used to create these models by trial-and-error, but it’s a slow process. Now, researchers have created a special kind of artificial intelligence (AI) that can quickly discover and create these models. This AI is called PKINNs. It looks at large amounts of data and uses math to figure out how medicines work in the body. The models created by PKINNs are easy to understand and explain. This new way of creating pharmacometric models could make it easier to develop new medicines. |
Keywords
» Artificial intelligence » Neural network » Regression