DigiChemTree enables programmable light-induced carbene generation for on demand chemical synthesis.

The reproducibility of chemical reactions, when obtaining protocols from literature or databases, is highly challenging for academicians, industry professionals and even now for the machine learning process. To synthesize the organic molecule under the photochemical condition, several years for the reaction optimization, highly skilled manpower, long reaction time etc. are needed, resulting in non-affordability and slow down the research and development. Herein, we have introduced the DigiChemTree backed with the artificial intelligence to auto-optimize the photochemical reaction parameter and synthesizing the on demand library of the molecules in fast manner. Newly, auto-generated digital code was further tested for the late stage functionalization of the various active pharmaceutical ingredient.

Continuous and autonomous-flow separation of laccase enzyme utilizing functionalized aqueous two-phase system with computer vision control.

Downstream processing of biomolecules, particularly therapeutic proteins and enzymes, presents a formidable challenge due to intricate unit operations and high costs. This study introduces a novel cysteine (cys) functionalized aqueous two-phase system (ATPS) utilizing polyethylene glycol (PEG) and potassium phosphate, referred as PEG-K3PO4/cys, for selective extraction of laccase from complex protein mixtures. A 3D-baffle micro-mixer and phase separator was meticulously designed and equipped with computer vision controller, to enable precise mixing and continuous phase separation under automated-flow. Microfluidic-assisted ATPS exhibits substantial increase in partition coefficient (Kflow = 16.3) and extraction efficiency (EEflow = 88 %) for laccase compared to conventional batch process. Integrated and continuous-flow process efficiently partitioned laccase, even in low concentrations and complex crude extracts. Circular dichroism spectra of laccase confirm structural stability of enzyme throughout the purification process. Eventually, continuous-flow microfluidic bioseparation is highly useful for seamless downstream processing of target biopharmaceuticals in integrated and autonomous manner.

Auto-Optimized Electro-Flow Reactor Platform for the in-situ Reduction of P(V) Oxide to P(III) and Their Application.

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. Herein, we report an auto-optimized electro-micro-flow reactor platform for the in-situ reduction of stable P(V) oxide to sensitive P(III) and further utilized the method for Corey-Fuchs reaction.

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals.

Biopharmaceuticals have emerged as powerful therapeutic agents, revolutionizing the treatment landscape for various diseases, including cancer, infectious diseases, autoimmune and genetic disorders. These biotherapeutics pave the way for precision medicine with their unique and targeted capabilities. The production of high-quality biologics entails intricate manufacturing processes, including cell culture, fermentation, purification, and formulation, necessitating specialized facilities and expertise. These complex processes are subject to rigorous regulatory oversight to evaluate the safety, efficacy, and quality of biotherapeutics prior to clinical approval. Consequently, these drugs undergo extensive purification unit operations to achieve high purity by effectively removing impurities and contaminants. The field of personalized precision medicine necessitates the development of novel and highly efficient technologies. Microfluidic technology addresses unmet needs by enabling precise and compact separation, allowing rapid, integrated and continuous purification modules. Moreover, the integration of intelligent biomanufacturing systems with miniaturized devices presents an opportunity to significantly enhance the robustness of complex downstream processing of biopharmaceuticals, with the benefits of automation and advanced control. This allows seamless data exchange, real-time monitoring, and synchronization of purification steps, leading to improved process efficiency, data management, and decision-making. Integrating autonomous systems into biopharmaceutical purification ensures adherence to regulatory standards, such as good manufacturing practice (GMP), positioning the industry to effectively address emerging market demands for personalized precision nano-medicines. This perspective review will emphasize on the significance, challenges, and prospects associated with the adoption of continuous, integrated, and intelligent methodologies in small-scale downstream processing for various types of biologics. By utilizing microfluidic technology and intelligent systems, purification processes can be enhanced for increased efficiency, cost-effectiveness, and regulatory compliance, shaping the future of biopharmaceutical production and enabling the development of personalized and targeted therapies.

Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis.

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. By the comparison of the performance of MO with that of various BOs on four datasets of different flow syntheses, it was verified that MO consistently performs the best-in-class for all emulators developed through machine learning, while the conventional BOs based on surrogate models such as the Gaussian process, random forest, neural network ensemble, and gradient boosting demonstrated varying performances from each emulator, which implies that benchmarking is required.