This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data.
Optimization of a synthetic reaction with respect to solvent choice and operating conditions was implemented as a machine learning-based workflow. The approach is exemplified on the case study of selection of a promising solvent to maximize the yield of a Mitsunobu reaction producing isopropyl benzoate. A solvent was defined with 15 molecular descriptors, and a library of solvent descriptors was built. The descriptors were converted into a reduced dimensionality form using an Autoencoder. Experimental yields were used to train a multilayered artificial neural network (ANN) surrogate model, which was used for the optimization and design of experiments (DoE). DoE was performed in an active learning mode to reduce the number of experiments required for reaction optimization. The final surrogate model identified 1-chloropentane as a promising solvent, which resulted in an experimental yield of 93%.
Heavy metal ions (HMIs) and radionuclides pose serious threats to food safety, human health, and marine ecosystems. Titanium carbides are advantageous owing to their good hydrophilicity, controllable surface charge, specific active groups, and high radiation stability, which make them effective candidates for removing toxic HMIs and radionuclides from wastewater. Recently, a lot of research is conducted to discover new methods for preparing functional titanium carbide composite materials to enhance the adsorption performance and overcome the shortcomings of conventional nanomaterials. Since 2011, the titanium carbide‐based adsorbents have undergone a developmental process from a single 2D nanosheet to a wide range of composite materials and 3D architectures. In this review, the development and progress in the design and synthesis methods of various titanium carbide‐based adsorbents are summarized. These methods are practical, scalable, and controllable in terms of structure and surface chemistry. The application of these surface‐modified composite materials in the adsorption of HMIs and radionuclides is also discussed, and the adsorption mass transfer mechanism of the adsorbates on titanium carbide‐based adsorbents is analyzed. Finally, the challenges and prospects of titanium carbide composite materials for future applications in the domain of metal ion adsorption are discussed.
Computer Aided Synthesis Planning (CASP) development of reaction routes requires understanding of complete reaction structures. However, most reactions in the current databases are missing reaction co-participants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace atom rearrangements in reactions, they fail to identify the missing molecules to complete reactions. This is because these approaches are data-driven models trained on the current reaction databases which comprise of incomplete reactions. In this work, a workflow was developed to tackle the reaction completion challenge. This includes a heuristic-based method to identify the balanced reactions from reaction databases and complete some imbalanced reactions by adding candidate molecules. A machine learning masked language model (MLM) was trained to learn from reaction SMILES sentences of these completed reactions. The model predicted missing molecules for the incomplete reactions; a workflow analogous to predicting missing words in sentences. The model is promising for prediction of small and middle size missing molecules in incomplete reaction records. The workflow combining both the heuristic and the machine learning methods completed more than half of the entire reaction space.
Computer assisted synthesis planning (CASP) accelerates the development of organic synthesis routes of pharmaceuticals and industrial chemicals. CASP tools are generally developed on the rules or data of synthetic chemistry which include some enzymatic reactions. However, synthetic biology offers a new degree of freedom through the potential to engineer new synthetic steps. In this work, we present a method to hybridise conventional organic synthesis and synthetic biology to guide synthesis planning. A section of organic reactions from Reaxys® database was combined with metabolic reactions from KEGG database as reactions pools. The combined database was used to assemble synthetic pathways from multiple building blocks to a target molecule. The routes assembly was performed using reinforcement learning, which was adapted to learn the values of molecular structures in synthesis planning, and to develop a policy model to suggest near-optimal multi-step synthesis route choices from the available reactions pool. To quantify the added value of synthetic biology in the hybrid routes, three policy model ‘decision makers’ were developed from the organic, biological and hybrid reactions pools respectively. The near-optimal synthetic routes planned from the three reactions pools were evaluated and compared to discuss the benefits of the hybrid synthetic chemistry plus synthetic biology decision space in reaction routes optimisation.
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