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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Related Experiment Video

Updated: Sep 10, 2025

Synthesis of a Borylated Ibuprofen Derivative Through Suzuki Cross-Coupling and Alkene Boracarboxylation Reactions
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Kinetic Modeling and Multiobjective Optimization of Ibuprofen Synthesis Using Machine Learning.

Lang Xiang1, Pengfei Qu2

  • 1School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.

ACS Omega
|August 25, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models optimize ibuprofen synthesis by predicting reaction outcomes and costs. Key factors like catalyst concentration are identified, leading to strategies for efficient, cost-effective drug production.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Machine Learning Applications

Background:

  • Ibuprofen synthesis requires precise control over reaction parameters for efficiency and cost-effectiveness.
  • Traditional optimization methods can be time-consuming and may not capture complex parameter interactions.

Purpose of the Study:

  • To develop and apply integrated machine learning tools for modeling and optimizing the ibuprofen synthesis process.
  • To identify critical input variables and optimal operating conditions for ibuprofen production.

Main Methods:

  • Creation of a large database (39,460 combinations) using chemical reaction theory, validated experimentally.
  • Application of a CatBoost meta-model optimized with a snow ablation optimizer.
  • Utilizing SHAP values for variable importance analysis and NSGA-II for multiobjective optimization.
  • Performing Monte Carlo simulations for uncertainty analysis.

Main Results:

  • The optimized CatBoost model accurately predicts reaction time, conversion rate, and production cost.
  • Critical parameters identified include catalyst precursor (L2PdCl2), H+, and H2O concentrations.
  • Optimal catalyst concentration range (0.002-0.01 mol/m3) found for high conversion and low cost.
  • Reaction time shows high sensitivity to parameter fluctuations, with nonlinear behavior.

Conclusions:

  • Integrated machine learning effectively models and optimizes ibuprofen synthesis.
  • Data-driven insights provide quantitative guidance for rational process design.
  • The study demonstrates a powerful approach combining physics-based modeling with machine learning for chemical process optimization.