Publications by authors named "Amirreza Mottafegh"
Article Synopsis
- Reproducibility in chemical reactions is difficult for researchers and industry professionals due to the need for extensive optimization, skilled labor, and lengthy reaction times, which hinder research and development.
- The DigiChemTree platform uses artificial intelligence to automatically optimize photochemical reaction parameters, allowing for faster synthesis of organic molecules.
- The digital code generated by DigiChemTree was successfully tested for late-stage functionalization of various active pharmaceutical ingredients, demonstrating its effectiveness in speeding up the process.
Trivalent phosphine catalysis is mostly utilized to activate the carbon-carbon multiple bonds to form carbanion intermediate species and is highly sensitive to certain variables. Random manual multi-variables are critical for understanding the batch disabled regeneration of trivalent phosphine chemistry. We need the artificial intelligence-based system which can change the variable based on previously conducted failed experiment.
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- Downstream processing of therapeutic proteins like laccase is challenging due to complex procedures and high costs, prompting the development of a new extraction system.
- The study presents a cysteine-functionalized aqueous two-phase system (ATPS) using PEG and potassium phosphate, which shows improved extraction efficiency and partitioning for laccase in comparison to traditional methods.
- A specialized micro-mixer and phase separator enhance the automation and precision of this technology, confirming the structural stability of the enzyme during purification, making it a promising approach for efficient biopharmaceutical processing.
Article Synopsis
- * The rigorous regulatory oversight ensures the safety and quality of these biologics, which undergo thorough purification to remove impurities and meet clinical approval standards.
- * Advancements in microfluidic technology and intelligent biomanufacturing systems enhance the efficiency of biopharmaceutical purification, allowing for automated processes, real-time monitoring, and adherence to good manufacturing practices to meet the demand for personalized medicines.
Optimizing a wide range of reaction parameters, steps, and pathways is currently considered one of the most complex and challenging problems in microflow-based organic synthesis. As a novel solution, Bayesian optimization (BO) has been utilized to efficiently guide the optimized conditions of flow reactors; however, the benchmarking process for selecting the optimal model among various surrogate models remains inefficient. In this work, we report meta optimization (MO) by benchmarking multiple surrogate models in real-time without any pre-work, which is realized by evaluating the expected values obtained by the regressor used to build each surrogate model, enabling efficient optimization of reaction conditions.
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