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.

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.

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.

Patterns of patients with polypharmacy in adult population from Korea.

Polypharmacy and its rising global prevalence is a growing public health burden. Using a large representative nationwide Korean cohort (N = 761,145), we conducted a retrospective cross-sectional study aiming to identify subpopulations of patients with polypharmacy and characterize their unique patterns through cluster analysis. Patients aged ≥ 30 years who were prescribed at least one medication between 2014 and 2018 were included in our study. Six clusters were identified: cluster 1 mostly included patients who were hospitalized for a long time (4.3 ± 5.3 days); cluster 2 consisted of patients with disabilities (100.0%) and had the highest mean number of prescription drugs (7.7 ± 2.8 medications); cluster 3 was a group of low-income patients (99.9%); cluster 4 was a group of high-income patients (80.2%) who frequently (46.4 ± 25.9 days) visited hospitals/clinics (7.3 ± 2.7 places); cluster 5 was mostly elderly (74.9 ± 9.8 years) females (80.3%); and cluster 6 comprised mostly middle-aged (56.4 ± 1.5 years) males (88.6%) (all P < 0.001). Patients in clusters 1-5 had more prescribed medications and outpatient visit days than those in cluster 6 (all P < 0.001). Given limited health care resources, individuals with any of the identified phenotypes may be preferential candidates for participation in intervention programs for optimal medication use.

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.

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.

Inverse association between dietary fiber intake and depression in premenopausal women: a nationwide population-based survey.

OBJECTIVE: Depression is among the most common neuropsychiatric disorders, and its prevalence is twofold higher in women than in men. This study aimed to investigate the relationship between dietary fiber intake and depression in women by menopause status using data from a nationwide population-based survey conducted in Korea. METHODS: We utilized the Korea National Health and Nutritional Examination Survey data for 2014, 2016, and 2018 with a complex sampling design. Dietary fiber intake was calculated according to the 24-hour recall method, and we used Patient Health Questionnaire-9 scores to assess depression. A t test based on the general linear model was used to compare mean dietary fiber intake according to the presence of depression by menopause status. A logistic regression analysis was conducted to compute the odds ratio for depression according to the gradually adjusted model. RESULTS: This study included 5,807 women. Among the premenopausal women, dietary fiber intake was higher in the nondepression group than in the depression group (P < 0.001), while there was no significant difference among postmenopausal women. Accordingly, among the premenopausal women, a significantly inverse relationship was observed between a change in daily dietary fiber intake as 1 g/1,000 kcal and the prevalence of depression in the fully adjusted model with an odds ratio of 0.949 (95% confidence interval, 0.906-0.993; P = 0.03). However, among the postmenopausal women, this significant association was not observed. CONCLUSIONS: Dietary fiber intake was inversely associated with depression in premenopausal but not postmenopausal women.

Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules.

The stabilities of radicals play a central role in determining the thermodynamics and kinetics of many reactions in organic chemistry. In this data descriptor, we provide consistent and validated quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules containing C, H, N and O atoms. These data consist of optimized 3D geometries, enthalpies, Gibbs free energy, vibrational frequencies, Mulliken charges and spin densities calculated at the M06-2X/def2-TZVP level of theory, which was previously found to have a favorable trade-off between experimental accuracy and computational efficiency. We expect this data to be useful in the further development of machine learning techniques to predict reaction pathways, bond strengths, and other phenomena closely related to organic radical chemistry.

A perspective on biomass-derived biofuels: From catalyst design principles to fuel properties.

The hazards to health and the environment associated with the transportation sector include smog, particulate matter, and greenhouse gas emissions. Conversion of lignocellulosic biomass into biofuels has the potential to provide significant amounts of infrastructure-compatible liquid transportation fuels that reduce those hazardous materials. However, the development of these technologies is inefficient, due to: (i) the lack of a priori fuel property consideration, (ii) poor shared vocabulary between process chemists and fuel engineers, and (iii) modern and future engines operating outside the range of traditional autoignition metrics such as octane or cetane numbers. In this perspective, we describe an approach where we follow a "fuel-property first" design methodology with a sequence of (i) identifying the desirable fuel properties for modern engines, (ii) defining molecules capable of delivering those properties, and (iii) designing catalysts and processes that can produce those molecules from a candidate feedstock in a specific conversion process. Computational techniques need to be leveraged to minimize expenses and experimental efforts on low-promise options. This concept is illustrated with current research information available for biomass conversion to fuels via catalytic fast pyrolysis and hydrotreating; outstanding challenges and research tools necessary for a successful outcome are presented.

Publisher Correction: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

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.

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.

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.

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.

2-Phenylpyridine- and 2-(benzo[b]thiophen-2-yl)pyridine-based o-carboranyl compounds: impact of the structural formation of aromatic rings on photophysical properties.

2-Phenylpyridine- and 2-(benzo[b]thiophen-2-yl)pyridine-based (ppy- and btp-based) o-carboranyl (Car1 and Car2) and their B(CH3)2-C∧N-chelated (Car1B and Car2B) compounds were prepared and fully characterised by multinuclear NMR spectroscopy and elemental analysis. The solid-state structure of Car2B was determined by single-crystal X-ray diffraction, which revealed a four-coordinated dimethylboryl centre. All compounds displayed major absorption bands that were assigned to π-π* transitions involving the ppy and btp moieties, as well as weak intramolecular charge-transfer (ICT) transitions between the o-carboranes and their aryl groups. Furthermore, the chelated compounds exhibited dominant low-energy absorption bands (λabs = 333 nm for Car1B and 383 nm for Car2B) resulting from the reinforcement of ICT transitions that correspond to the o-carborane moieties through the restriction of aromatic-ring free rotation. While Car1 and Car2 did not exhibit photoluminescence emissions in toluene at 298 K, Car1B and Car2B showed intense emissions, which are assignable to π-π* transitions associated with each chelated aryl group. However, Car1 and Car2 evidently emitted at around 450 nm in solution at 77 K, invoked by radiative ICT transitions between the carborane and the ppy or btp moiety, indicating that ICT-based radiative decay is only invigorated in the rigid state in the absence of structural variations, such as C-C bond fluctuations in the carborane cage and aromatic-ring free rotation. Interestingly, while Car1 in the film state exhibited a weak ICT-based emission spectrum, and Car1B and Car2B showed intense emissions originating from π-π* transitions associated with each chelated aryl group, Car2 showed significantly enhanced emissions in the same energy region as that exhibited in solution at 77 K, resulting in a much larger quantum efficiency over that in solution. DFT-optimised structures of Car1 and Car2 in their ground and the first-excited states clearly reveal that the enhanced emissive features of Car2 in the film state are strongly associated with the retained planarity of the btp moiety in both the ground and excited states. The photophysical results for these o-carboranyl compounds definitively reveal that the planarities of the aryl groups appended to the o-carborane decisively affect the efficiency of radiative decay based on ICT involving the o-carborane.

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.

Different Behaviors of a Substrate in P450 Decarboxylase and Hydroxylase Reveal Reactivity-Enabling Actors.

Biological routes to the production of fuels from renewable feedstocks hold significant promise in our efforts towards a sustainable future. The fatty acid decarboxylase enzyme (OleTJE) is a cytochrome P450 enzyme that converts long and medium chain fatty acids to terminal alkenes and shares significant similarities in terms of structure, substrate scope and mechanism with the hydroxylase cytochrome P450 (P450BSß). Recent reports have demonstrated that catalytic pathways in these enzymes bifurcate when the heme is in its iron-hydroxo (compound II) state. In spite of significant similarities, the fundamental underpinnings of their different characteristic wild-type reactivities remain ambiguous. Here, we develop point charges, modified parameters and report molecular simulations of this crucial intermediate step. Water occupancies and substrate mobility at the active site are observed to be vital differentiating aspects between the two enzymes in the compound II state and corroborate recent experimental hypotheses. Apart from increased substrate mobility in the hydroxylase, which could have implications for enabling the rebound mechanism for hydroxylation, OleTJE is characterized by much stronger binding of the substrate carboxylate group to the active site arginine, implicating it as an important enabling actor for decarboxylation.

Structural and molecular dynamics studies of a C1-oxidizing lytic polysaccharide monooxygenase from Heterobasidion irregulare reveal amino acids important for substrate recognition.

Lytic polysaccharide monooxygenases (LPMOs) are a group of recently discovered enzymes that play important roles in the decomposition of recalcitrant polysaccharides. Here, we report the biochemical, structural, and computational characterization of an LPMO from the white-rot fungus Heterobasidion irregulare (HiLPMO9B). This enzyme oxidizes cellulose at the C1 carbon of glycosidic linkages. The crystal structure of HiLPMO9B was determined at 2.1 Å resolution using X-ray crystallography. Unlike the majority of the currently available C1-specific LPMO structures, the HiLPMO9B structure contains an extended L2 loop, connecting ß-strands ß2 and ß3 of the ß-sandwich structure. Molecular dynamics (MD) simulations suggest roles for both aromatic and acidic residues in the substrate binding of HiLPMO9B, with the main contribution from the residues located on the extended region of the L2 loop (Tyr20) and the LC loop (Asp205, Tyr207, and Glu210). Asp205 and Glu210 were found to be involved in the hydrogen bonding with the hydroxyl group of the C6 carbon of glucose moieties directly or via a water molecule. Two different binding orientations were observed over the course of the MD simulations. In each orientation, the active-site copper of this LPMO preferentially skewed toward the pyranose C1 of the glycosidic linkage over the targeted glycosidic bond. This study provides additional insight into cellulose binding by C1-specific LPMOs, giving a molecular-level picture of active site substrate interactions. DATABASE: The atomic coordinates and structure factors for HiLPMO9B have been deposited in the Protein Data Bank with accession code 5NNS.

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.