Short and scalable synthesis of cynandione A.

A two-step gram-scale synthesis of cynandione A is described. The key to success is the one-pot tandem oxidation/regioselective arylation of 1,4-hydroquinone in the presence of an excess amount of oxidant. Natural bond orbital charge analysis was performed in order to understand the regioselectivity of the arylation step. The highly practical and scalable synthesis developed herein is expected to assist the in-depth biological evaluation of cynandione A in various animal models.

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.

Performance-advantaged ether diesel bioblendstock production by a priori design.

Lignocellulosic biomass offers a renewable carbon source which can be anaerobically digested to produce short-chain carboxylic acids. Here, we assess fuel properties of oxygenates accessible from catalytic upgrading of these acids a priori for their potential to serve as diesel bioblendstocks. Ethers derived from C2 and C4 carboxylic acids are identified as advantaged fuel candidates with significantly improved ignition quality (>56% cetane number increase) and reduced sooting (>86% yield sooting index reduction) when compared to commercial petrodiesel. The prescreening process informed conversion pathway selection toward a C11 branched ether, 4-butoxyheptane, which showed promise for fuel performance and health- and safety-related attributes. A continuous, solvent-free production process was then developed using metal oxide acidic catalysts to provide improved thermal stability, water tolerance, and yields. Liter-scale production of 4-butoxyheptane enabled fuel property testing to confirm predicted fuel properties, while incorporation into petrodiesel at 20 vol % demonstrated 10% improvement in ignition quality and 20% reduction in intrinsic sooting tendency. Storage stability of the pure bioblendstock and 20 vol % blend was confirmed with a common fuel antioxidant, as was compatibility with elastomeric components within existing engine and fueling infrastructure. Technoeconomic analysis of the conversion process identified major cost drivers to guide further research and development. Life-cycle analysis determined the potential to reduce greenhouse gas emissions by 50 to 271% relative to petrodiesel, depending on treatment of coproducts.

Associations of truncal body composition with cognitive status in patients with dementia.

BACKGOUND: Central obesity in midlife is a risk factor of cognitive decline and dementia, and also one of the factors that make cognitive functions deteriorate rapidly. OBJECTIVE: The objective of this study is to investigate the relationship between truncal body composition (fat and muscle) and cognitive impairment in patients with dementia. METHODS: A total of 81 female over 60 years of age with probable Alzheimer's disease were recruited between November 2014 and September 2015. The Mini-Mental State Examination, Global Deterioration Scale, and Clinical Dementia Rating Scale were used to assess the cognitive functions. Both truncal fat and muscle mass were measured using body dual-energy X-ray absorptiometry and used as a percentage of body weight (TMM% and TFM%). Correlations between truncal composition and cognitive status were assessed by simple correlation analysis, which was followed by partial correlation analysis with age and educational years. RESULTS: TFM% was not related to cognitive impairment. In contrast, TMM% had a significantly negative correlation with all three cognitive assessment scores. After further adjusting for age, educational years, and vascular factors, there was still a relationship between TMM% and cognitive functions. CONCLUSIONS: Unlike truncal fat mass that showed no relevance with cognitive functions, the truncal muscle mass was negatively correlated with cognitive status. The truncal muscle mass is thought to affect cognitive status in dementia patients somehow.

Spirobifluorene-Based o-Carboranyl Compounds: Insights into the Rotational Effect of Carborane Cages on Photoluminescence.

9,9'-Spirobifluorene-based closo-o-carboranyl (SFC1 and SFC2) compounds and their nido-derivatives (nido-SFC1 and nido-SFC2) were prepared and characterized. The two closo-compounds displayed major absorption bands assignable to π-π* transitions involving the spirobifluorene group, as well as weak intramolecular charge-transfer (ICT) transitions between the o-carboranes and their spirobifluorene moieties. The nido-compounds exhibited slightly blueshifted absorption bands resulting from the absence of the ICT transitions corresponding to the o-carborane moieties due to the anionic character of the nido-o-carboranes. While SFC1 exhibited only high-energy emissions in THF at 298 K (only from locally excited (LE) states assignable to π-π* transitions on the spirobifluorene group), remarkable emissions in the low-energy region were observed in the rigid state such as in THF at 77 K and in the film state. SFC2 displayed intense emissions in the low-energy region in all states. The fact that neither of the nido-derivatives of SFC1 and SFC2 exhibited low-energy emissions and the TD-DFT calculation results of each closo-compound clearly verified that the low-energy emission was based on ICT-based radiative decay. The conformational barriers from each relative energy calculation upon changing the dihedral angles around the o-carborane cages for both compounds confirmed that the rotation of the o-carborane cages and terminal phenyl rings for SFC1 is freer than that for SFC2.

Reactive Molecular Dynamics Simulations and Quantum Chemistry Calculations To Investigate Soot-Relevant Reaction Pathways for Hexylamine Isomers.

Sooting tendencies of a series of nitrogen-containing hydrocarbons (NHCs) have been recently characterized experimentally using the yield sooting index (YSI) methodology. This work aims to identify soot-relevant reaction pathways for three selected C6H15N amines, namely, dipropylamine (DPA), diisopropylamine (DIPA), and 3,3-dimethylbutylamine (DMBA) using ReaxFF molecular dynamics (MD) simulations and quantum mechanical (QM) calculations and to interpret the experimentally observed trends. ReaxFF MD simulations are performed to determine the important intermediate species and radicals involved in the fuel decomposition and soot formation processes. QM calculations are employed to extensively search for chemical reactions involving these species and radicals based on the ReaxFF MD results and also to quantitatively characterize the potential energy surfaces. Specifically, ReaxFF simulations are carried out in the NVT ensemble at 1400, 1600, and 1800 K, where soot has been identified to form in the YSI experiment. These simulations account for the interactions among test fuel molecules and pre-existing radicals and intermediate species generated from rich methane combustion, using a recently proposed simulation framework. ReaxFF simulations predict that the reactivity of the amines decrease in the order DIPA > DPA > DMBA, independent of temperature. Both QM calculations and ReaxFF simulations predict that C2H4, C3H6, and C4H8 are the main nonaromatic soot precursors formed during the decomposition of DPA, DIPA, and DMBA, respectively, and the associated reaction pathways are identified for each amine. Both theoretical methods predict that sooting tendency increases in the order DPA, DIPA, and DMBA, consistent with the experimentally measured trend in YSI. This work demonstrates that sooting tendencies and soot-relevant reaction pathways of fuels with unknown chemical kinetics can be identified efficiently through combined ReaxFF and QM simulations. Overall, predictions from ReaxFF simulations and QM calculations are consistent, in terms of fuel reactivity, major intermediates, and major nonaromatic soot precursors.

Photophysical Properties of Spirobifluorene-Based o-Carboranyl Compounds Altered by Structurally Rotating the Carborane Cages.

9,9'-Spirobifluorene-based o-carboranyl compounds C1 and C2 were prepared and fully characterized by multinuclear nuclear magnetic resonance (NMR) spectroscopy and elemental analysis. The solid-state structure of C1 was also determined by single-crystal X-ray diffractometry. The two carboranyl compounds display major absorption bands that are assigned to π-π* transitions involving their spirobifluorene groups, as well as weak intramolecular charge-transfer (ICT) transitions between the o-carboranes and their spirobifluorene groups. While C1 only exhibited high-energy emissions (λem = ca. 350 nm) in THF at 298 K due to locally excited (LE) states assignable to π-π* transitions involving the spirobifluorene group alone, a remarkable emission in the low-energy region was observed in the rigid state, such as in THF at 77 K or the film state. Furthermore, C2 displays intense dual emissive patterns in both high- and low-energy regions in all states. Electronic transitions that were calculated by time-dependent-DFT (TD-DFT) for each compound based on ground (S0) and first-excited (S1) state optimized structures clearly verify that the low-energy emissions are due to ICT-based radiative decays. Calculated energy barriers that are based on the relative energies associated with changes in the dihedral angle around the o-carborane cages in C1 and C2 clearly reveal that the o-carborane cage in C1 rotates more freely than that in C2. All of the molecular features indicate that ICT-based radiative decay is only available to the rigid state in the absence of structural fluctuations, in particular the free-rotation of the o-carborane cage.

Effect of Planarity of Aromatic Rings Appended to o-Carborane on Photophysical Properties: A Series of o-Carboranyl Compounds Based on 2-Phenylpyridine- and 2-(Benzo[b]thiophen-2-yl)pyridine.

Herein, we investigated the effect of ring planarity by fully characterizing four pyridine-based o-carboranyl compounds. o-Carborane was introduced to the C4 position of the pyridine rings of 2-phenylpyridine and 2-(benzo[b]thiophen-2-yl)pyridine (CB1 and CB2, respectively), and the compounds were subsequently borylated to obtain the corresponding CN-chelated compounds CB1B and CB2B. Single-crystal X-ray diffraction analysis of the molecular structures of CB2 and CB2B confirmed that o-carborane is appended to the aryl moiety. In photoluminescence experiments, CB2, but not CB1, showed an intense emission, assignable to intramolecular charge transfer (ICT) transition between the aryl and o-carborane moieties, in both solution and film states. On the other hand, in both solution and film states, CB1B and CB2B demonstrated a strong emission, originating from π-π * transition in the aryl groups, that tailed off to 650 nm owing to the ICT transition. All intramolecular electronic transitions in these o-carboranyl compounds were verified by theoretical calculations. These results distinctly suggest that the planarity of the aryl groups have a decisive effect on the efficiency of the radiative decay due to the ICT transition.

Through-Space Ultrafast Photoinduced Electron Transfer Dynamics of a C70-Encapsulated Bisporphyrin Covalent Organic Polyhedron in a Low-Dielectric Medium.

Ultrafast photoinduced electron transfer (PIET) dynamics of a C70-encapsulated bisporphyrin covalent organic polyhedron hybrid (C70@COP-5) is studied in a nonpolar toluene medium with fluorescence and transient absorption spectroscopies. This structurally rigid donor (D)-acceptor (A) molecular hybrid offers a new platform featuring conformationally predetermined cofacial D-A orientation with a fixed edge-to-edge separation, REE (2.8 Å), without the aid of covalent bonds. Sub-picosecond PIET (τET ≤ 0.4 ps) and very slow charge recombination (τCR ≈ 600 ps) dynamics are observed. The origin of these dynamics is discussed in terms of enhanced D-A coupling (V = 675 cm-1) and extremely small reorganization energy (λ ≈ 0.18 eV), induced by the intrinsic structural rigidity of the C70@COP-5 complex.

Understanding Trends in Autoignition of Biofuels: Homologous Series of Oxygenated C5 Molecules.

Oxygenated biofuels provide a renewable, domestic source of energy that can enable adoption of advanced, high-efficiency internal combustion engines, such as those based on homogeneously charged compression ignition (HCCI). Of key importance to such engines is the cetane number (CN) of the fuel, which is determined by the autoignition of the fuel under compression at relatively low temperatures (550-800 K). For the plethora of oxygenated biofuels possible, it is desirable to know the ignition delay times and the CN of these fuels to help guide conversion strategies so as to focus efforts on the most desirable fuels. For alkanes, the chemical pathways leading to radical chain-branching reactions giving rise to low-temperature autoignition are well-known and are highly coincident with the buildup of reactive radicals such as OH. Key in the mechanisms leading to chain branching are the addition of molecular oxygen to alkyl radicals and the rearrangement and dissociation of the resulting peroxy radials. Prediction of the temperature and pressure dependence of reactions that lead to the buildup of reactive radicals requires a detailed understanding of the potential energy surfaces (PESs) of these reactions. In this study, we used quantum mechanical modeling to systematically compare the effects of oxygen functionalities on these PESs and associated kinetics so as to understand how they affect experimental trends in autoignition and CN. The molecules studied here include pentane, pentanol, pentanal, 2-heptanone, methylpentyl ether, methyl hexanoate, and pentyl acetate. All have a saturated five-carbon alkyl chain with an oxygen functional group attached to the terminal carbon atom. The results of our systematic comparison may be summarized as follows: (1) Oxygen functionalities activate C-H bonds by lowering the bond dissociation energy (BDE) relative to alkanes. (2) The R-OO bonds in peroxy radicals adjacent to carbonyl groups are weaker than corresponding alkyl systems, leading to dissociation of ROO• radicals and reducing reactivity and hence CN. (3) Hydrogen atom transfer in peroxy radicals is important in autoignition, and low barriers for ethers and aldehydes lead to high CN. (4) Peroxy radicals formed from alcohols have low barriers to form aldehydes, which reduce the reactivity of the alkyl radical. These findings for the formation and reaction of alkyl radicals with molecular oxygen explain the trend in CN for these common biofuel functional groups.

Quantum mechanical calculations suggest that lytic polysaccharide monooxygenases use a copper-oxyl, oxygen-rebound mechanism.

Lytic polysaccharide monooxygenases (LPMOs) exhibit a mononuclear copper-containing active site and use dioxygen and a reducing agent to oxidatively cleave glycosidic linkages in polysaccharides. LPMOs represent a unique paradigm in carbohydrate turnover and exhibit synergy with hydrolytic enzymes in biomass depolymerization. To date, several features of copper binding to LPMOs have been elucidated, but the identity of the reactive oxygen species and the key steps in the oxidative mechanism have not been elucidated. Here, density functional theory calculations are used with an enzyme active site model to identify the reactive oxygen species and compare two hypothesized reaction pathways in LPMOs for hydrogen abstraction and polysaccharide hydroxylation; namely, a mechanism that employs a η(1)-superoxo intermediate, which abstracts a substrate hydrogen and a hydroperoxo species is responsible for substrate hydroxylation, and a mechanism wherein a copper-oxyl radical abstracts a hydrogen and subsequently hydroxylates the substrate via an oxygen-rebound mechanism. The results predict that oxygen binds end-on (η(1)) to copper, and that a copper-oxyl-mediated, oxygen-rebound mechanism is energetically preferred. The N-terminal histidine methylation is also examined, which is thought to modify the structure and reactivity of the enzyme. Density functional theory calculations suggest that this posttranslational modification has only a minor effect on the LPMO active site structure or reactivity for the examined steps. Overall, this study suggests the steps in the LPMO mechanism for oxidative cleavage of glycosidic bonds.

Body volume, body fatness, and metabolic syndrome.

Body volume by three-dimensional body scanning (3DBS) may be an alternative index for evaluating body fatness. The aim of this study was to evaluate the association of body volume with body fatness and metabolic syndrome. This study included 38 Korean women whose body volume was measured using 3DBS. We measured body fatness using dual-energy X-ray absorptiometry and computed tomography. Participants with metabolic syndrome were defined as having three or more of the following components: high blood pressure (≥130/85 mmHg), elevated fasting glucose (≥100 mg/dl), hypertriglyceridemia (≥150 mg/dl), low high-density lipoprotein-cholesterol (<50 mg/dl), and abdominal obesity measured by waist circumference ≥80 cm. Total body, trunk, lower trunk, and limb volumes were significantly correlated with body mass index, waist circumference, total fat mass, percentage body fat, and abdominal fat areas. After adjustment for age, current smoking, at-risk drinking, and physical inactivity, the odds ratios (95% confidence intervals) for metabolic syndrome associated with total body, trunk, lower trunk, and limb volume were 1.08 (1.01-1.16), 1.11 (1.01-1.22), 1.20 (1.01-1.43), and 1.31 (1.04-1.66), respectively. Body volume by 3DBS was significantly associated with body fatness and metabolic syndrome. 3DBS may be a useful tool for detecting and monitoring body fatness and metabolic syndrome.

Structural and electronic snapshots during the transition from a Cu(II) to Cu(I) metal center of a lytic polysaccharide monooxygenase by X-ray photoreduction.

Lytic polysaccharide monooxygenases (LPMOs) are a recently discovered class of enzymes that employ a copper-mediated, oxidative mechanism to cleave glycosidic bonds. The LPMO catalytic mechanism likely requires that molecular oxygen first binds to Cu(I), but the oxidation state in many reported LPMO structures is ambiguous, and the changes in the LPMO active site required to accommodate both oxidation states of copper have not been fully elucidated. Here, a diffraction data collection strategy minimizing the deposited x-ray dose was used to solve the crystal structure of a chitin-specific LPMO from Enterococcus faecalis (EfaCBM33A) in the Cu(II)-bound form. Subsequently, the crystalline protein was photoreduced in the x-ray beam, which revealed structural changes associated with the conversion from the initial Cu(II)-oxidized form with two coordinated water molecules, which adopts a trigonal bipyramidal geometry, to a reduced Cu(I) form in a T-shaped geometry with no coordinated water molecules. A comprehensive survey of Cu(II) and Cu(I) structures in the Cambridge Structural Database unambiguously shows that the geometries observed in the least and most reduced structures reflect binding of Cu(II) and Cu(I), respectively. Quantum mechanical calculations of the oxidized and reduced active sites reveal little change in the electronic structure of the active site measured by the active site partial charges. Together with a previous theoretical investigation of a fungal LPMO, this suggests significant functional plasticity in LPMO active sites. Overall, this study provides molecular snapshots along the reduction process to activate the LPMO catalytic machinery and provides a general method for solving LPMO structures in both copper oxidation states.

Ethanol dehydration in HZSM-5 studied by density functional theory: evidence for a concerted process.

Dehydration over acidic zeolites is an important reaction class for the upgrading of biomass pyrolysis vapors to hydrocarbon fuels or to precursors for myriad chemical products. Here, we examine the dehydration of ethanol at a Brønsted acid site, T12, found in HZSM-5 using density functional theory (DFT). The geometries of both cluster and mixed quantum mechanics/molecular mechanics (QM:MM) models are prepared from the ZSM-5 crystal structure. Comparisons between these models and different DFT methods are conducted to show similar results among the models and methods used. Inclusion of the full catalyst cavity through a QM:MM approach is found to be important, since activation barriers are computed on average as 7 kcal mol(-1) lower than those obtained with a smaller cluster model. Two different pathways, concerted and stepwise, have been considered when examining dehydration and deprotonation steps. The current study shows that a concerted dehydration process is possible with a lower (4-5 kcal mol(-1)) activation barrier while previous literature studies have focused on a stepwise mechanism. Overall, this work demonstrates that fairly high activation energies (∼50 kcal mol(-1)) are required for ethanol dehydration. A concerted mechanism is favored over a stepwise mechanism because charge separation in the transition state is minimized. QM:MM approaches appear to provide superior results to cluster calculations due to a more accurate representation of charges on framework oxygen atoms.

Carbocation Stability in H-ZSM5 at High Temperature.

Zeolites are common catalysts for multiple industrial applications, including alcohol dehydration to produce olefins, and given their commercial importance, reaction mechanisms in zeolites have long been proposed and studied. Some proposed reaction mechanisms for alcohol dehydration exhibit noncyclic carbocation intermediates or transition states that resemble carbocations, and several previous studies suggest that the tert-butyl cation is the only noncyclic cation more stable than the corresponding chemisorbed species with the hydrocarbon bound to the framework oxygen (i.e., an alkoxide). To determine if carbocations can exist at high temperatures in zeolites, where these catalysts are finding new applications for biomass vapor-phase upgrading (∼500 °C), the stability of carbocations and the corresponding alkoxides were calculated with two ONIOM embedding methods (M06-2X/6-311G(d,p):M06-2X/3-21G) and (PBE-D3/6-311G(d,p):PBE-D3/3-21G) and plane-wave density functional theory (DFT) using the PBE functional corrected with entropic and Tkatchenko-Scheffler van der Waals corrections. The embedding methods tested are unreliable at finding minima for primary carbocations, and only secondary or higher carbocations can be described with embedding methods consistent with the periodic DFT results. The relative energy between the carbocations and alkoxides differs significantly between the embedding and the periodic DFT methods. The difference is between ∼0.23 and 14.30 kcal/mol depending on the molecule, the model, and the functional chosen for the embedding method. At high temperatures, the pw-DFT calculations predict that the allyl, isopropyl, and sec-butyl cations exhibit negligible populations while acetyl and tert-butyl cations exhibit significant populations (>10%). Moreover, the periodic DFT results indicate that mechanisms including secondary and tertiary carbocations intermediates or carbocations stabilized by adjacent oxygen or double bonds are possible at high temperatures relevant to some industrial uses of zeolite catalysts, although as the minority species in most cases.

Crystal structure and computational characterization of the lytic polysaccharide monooxygenase GH61D from the Basidiomycota fungus Phanerochaete chrysosporium.

Carbohydrate structures are modified and degraded in the biosphere by a myriad of mostly hydrolytic enzymes. Recently, lytic polysaccharide mono-oxygenases (LPMOs) were discovered as a new class of enzymes for cleavage of recalcitrant polysaccharides that instead employ an oxidative mechanism. LPMOs employ copper as the catalytic metal and are dependent on oxygen and reducing agents for activity. LPMOs are found in many fungi and bacteria, but to date no basidiomycete LPMO has been structurally characterized. Here we present the three-dimensional crystal structure of the basidiomycete Phanerochaete chrysosporium GH61D LPMO, and, for the first time, measure the product distribution of LPMO action on a lignocellulosic substrate. The structure reveals a copper-bound active site common to LPMOs, a collection of aromatic and polar residues near the binding surface that may be responsible for regio-selectivity, and substantial differences in loop structures near the binding face compared with other LPMO structures. The activity assays indicate that this LPMO primarily produces aldonic acids. Last, molecular simulations reveal conformational changes, including the binding of several regions to the cellulose surface, leading to alignment of three tyrosine residues on the binding face of the enzyme with individual cellulose chains, similar to what has been observed for family 1 carbohydrate-binding modules. A calculated potential energy surface for surface translation indicates that P. chrysosporium GH61D exhibits energy wells whose spacing seems adapted to the spacing of cellobiose units along a cellulose chain.

Crystal structure of glycoside hydrolase family 127 ß-l-arabinofuranosidase from Bifidobacterium longum.

Enzymes acting on ß-linked arabinofuranosides have been unknown until recently, in spite of wide distribution of ß-l-arabinofuranosyl oligosaccharides in plant cells. Recently, a ß-l-arabinofuranosidase from the glycoside hydrolase family 127 (HypBA1) was discovered in the newly characterized degradation system of hydroxyproline-linked ß-l-arabinooligosaccharides in the bacterium Bifidobacterium longum. Here, we report the crystal structure of HypBA1 in the ligand-free and ß-l-arabinofuranose complex forms. The structure of HypBA1 consists of a catalytic barrel domain and two additional ß-sandwich domains, with one ß-sandwich domain involved in the formation of a dimer. Interestingly, there is an unprecedented metal-binding motif with Zn(2+) coordinated by glutamate and three cysteines in the active site. The glutamate residue is located far from the anomeric carbon of the ß-l-arabinofuranose ligand, but one cysteine residue is appropriately located for nucleophilic attack for glycosidic bond cleavage. The residues around the active site are highly conserved among GH127 members. Based on biochemical experiments and quantum mechanical calculations, a possible reaction mechanism involving cysteine as the nucleophile is proposed.

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Simple and Efficient Enantioselective α-Deuteration Method of α-Amino Acids without External Chiral Sources.

Deuterium-labeled α-amino acids are useful in research related to drug discovery and biomedical science. However, a high degree of site selectivity and stereoselectivity in the deuterium incorporation process is still difficult to achieve. Herein, we report a new enantioselective deuteration method at the α-position of several amino acids without external chiral sources. The proposed deuteration methods (NaOEt and EtOD) are highly selective and simple. Additionally, we provide a mechanistic study for this enantioretentive deuteration.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.