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highlights:2026:mpc_fno [2026/04/09 13:46] – Didier Barradas Bautistahighlights:2026:mpc_fno [2026/04/09 14:02] (current) – Didier Barradas Bautista
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 ===== Research Breakthrough ===== ===== Research Breakthrough =====
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-Protein–protein docking generates thousands of possible interaction models—but only a few are correct. In our latest collaborative work, we introduce Iter-CONSRANK, an iterative scoring algorithm that filters out incorrect models and enriches the ensemble with correct ones. +    * Bridging the Real-Time Gap: Developed an AI framework that replaces slow, traditional physics simulations with Fourier Neural Operators, enabling the first real-time control of complex multiphase flows. 
-  * 📊 Tested on two challenging datasets, Iter-CONSRANK increased the fraction of correct models by up to 8× for medium-difficulty targets and outperformed over 150 scoring functions in ranking accuracy. +    * Precision AI Decision-Making: Integrated Bayesian Optimization with neural surrogates to precisely track liquid levels in bubble columns, overcoming the mathematical "noise" that typically breaks standard control algorithms. 
-  * 🎯 The method is available for use in pre-processing docking ensembles or as an independent scoring tool. +    * Accelerating Industrial Innovation: Reduced the computational cost of multiphase flow management by orders of magnitude, providing a scalable foundation for smarter, more efficient chemical reactors and energy systems.
-  * 🔬 This work was led in collaboration with Luigi Cavallo and our partners at the University of Naples “Parthenope”+
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 <fs 18>**🔬 Research Visualization**</fs> <fs 18>**🔬 Research Visualization**</fs>
-The graphical abstract below illustrates the iterative filtering strategy used to enrich correct protein–protein docking models. The paper entitled "Increasing the Fraction of Correct Solutions in Ensembles of Protein-Protein Docking Models by an Iterative Consensus Algorithm" is available for download and review. [[https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.70314|here]].  + This graph show the acceleration of the code . You can access the paper[[https://doi.org/10.48550/arXiv.2603.25308|here]]. 
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 <fs 18>**🎯 KVL's Contribution:**</fs> <fs 18>**🎯 KVL's Contribution:**</fs>
-KVL's visualization scientist **Didier Barradas-Bautista** contributed significantly to the development and validation of the Iter-CONSRANK algorithm through:+KVL's visualization scientist **Didier Barradas-Bautista** contributed significantly to the implementation of algorithm through:
  
-  * Design and implementation of contact-based clustering to enhance scoring accuracy +  * Updating the code to efficientiyl use the GPU memory 
-  * Generation of the 3K-BM5up benchmark dataset using multiple docking tools +  * refactoring the code to output efficientyl the results 
-  * Optimization of iteration parameters and performance evaluation across difficulty categories +
-  * Comparative benchmarking against 157 scoring functions, demonstrating top performance +
-  * Public release of the Iter-CONSRANK software for community use +
-  * Active participation in CAPRI scoring rounds, validating the method in blind tests+
  
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