Optically active tertiary alcohols by biocatalysis

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. SCIENTIFIC CONTRIBUTION: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database.

Immobilization of Candida antarctica Lipase A onto Macroporous Resin NKA-9: Esterification and Glycerolysis Performance Study

In this study, lipase A from Candida antarctica (CALA) was immobilized onto the macroporous resin NKA-9. Immobilization conditions (pH, time and CALA concentration) were studied, enzymatic activity and immobilization efficiency (IE) up to 968.89 U/g and 53.19% were respectively obtained under optimal conditions (immobilization pH 5.0, time 5 h and CALA concentration at 30 mg/mL). Then, the NKA-9 supported CALA (CALA@NKA-9) samples were used to catalyze glycerolysis in solvent-free system. With 0.25 g of the present CALA@NKA-9 (soybean oil 3.52 g and glycerol 0.184 g) and after 12 h reaction at 50 °C, diacylglycerols (DAG) content up to 64.37% and triacylglycerols (TAG) conversion at 83.33% were obtained. The relationship between temperature and TAG conversion was LnV ₀ = 13.9310-6.4212/T for CALA@NKA-9. Meanwhile, the activation energy (Ea) of CALA@NKA-9 was calculated to be 53.39 kJ/mol. In addition, reusability in the glycerolysis reaction was also evaluated, and 57.82% of the initial glycerolysis activity was retained after 9 consecutive applications. Furthermore, the CALA@NKA-9 was also used to catalyze the esterification (esterification of fatty acids with glycerol), however, the present CALA@NKA-9 cannot initiate the esterification. Therefore, the present CALA@NKA-9 is shown to be potential for DAG production through glycerolysis reaction.

Stereoselective Synthesis of Terpenoids through Lipase-Mediated Resolution Approaches

This review article focuses on the scientific developments concerning the lipase-mediated synthesis of terpenoids that have been reported in the literature during the last twenty years. More specifically, this review describes in depth the resolution approaches that allow the preparation of the chiral building blocks used for the stereoselective synthesis of bioactive terpenoids. The synthetic methods that have given new and innovative perspectives from a scientific standpoint, and the preparative approaches that possess industrial importance, are described thoroughly.

Dynamic kinetic resolution of a tertiary alcohol

In spite of the tremendous success of dynamic kinetic resolutions for a broad range of compound classes, tertiary alcohols and their corresponding esters have still remained as one of the most challenging substrates for this type of process. This is due to the size and steric hindrance of tertiary alcohols as well as to the difficulty in finding reaction conditions for the racemization of such compounds being at the same time compatible with the resolution reaction, which preferably is carried out with an enzyme. In this study, the first example of a dynamic kinetic resolution of a racemic tertiary alcohol is presented. The desired synthesis of the resulting enantiomerically pure ester was achieved by combining a lipase-catalyzed kinetic resolution with an in situ racemization utilizing a bio-compatible oxovanadium-catalyst. First, the two individual reactions were examined, improved and adjusted to be compatible with each other. Subsequently, addition of both catalysts in tailor-made portions led to the desired combined process and delivered the product with >99% ee and a conversion exceeding 50%, thus proving such a desired dynamic kinetic resolution of a tertiary alcohol.