HSQC Spectra Simulation and Matching for Molecular Identification

In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git).

Suppressing Cis/Trans 'Ring-Flipping' in Organoaluminium(III)-2-Pyridyl Dimers-Design Strategies Towards Lewis Acid Catalysts for Alkene Oligomerisation

Owing to its high natural abundance compared to the commonly used transition (precious) metals, as well as its high Lewis acidity and ability to change oxidation state, aluminium has recently been explored as the basis for a range of single-site catalysts. This paper aims to establish the ground rules for the development of a new type of cationic alkene oligomerisation catalyst containing two Al(III) ions, with the potential to act co-operatively in stereoselective assembly. Five new dimers of the type [R2Al(2-py')]2 (R=Me, iBu; py'=substituted pyridyl group) with different substituents on the Al atoms and pyridyl rings have been synthesised. The formation of the undesired cis isomers can be suppressed by the presence of substituents on the 6-position of the pyridyl ring due to steric congestion, with DFT calculations showing that the selection of the trans isomer is thermodynamically controlled. Calculations show that demethylation of the dimers [Me2Al(2-py')]2 with Ph3C+ to the cations [{MeAl(2-py')}2(μ-Me)]+ is highly favourable and that the desired trans disposition of the 2-pyridyl ring units is influenced by steric effects. Preliminary experimental studies confirm that demethylation of [Me2Al(6-MeO-2-py)]2 can be achieved using [Ph3C][B(C6F5)4].

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8-46% of marketed chemicals based on 1-10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

Reaction dynamics as the missing puzzle piece: the origin of selectivity in oxazaborolidinium ion-catalysed reactions

The selectivity in a group of oxazaborolidinium ion-catalysed reactions between aldehyde and diazo compounds cannot be explained using transition state theory. VRAI-selectivity, developed to predict the outcome of dynamically controlled reactions, can account for both the chemo- and the stereo-selectivity in these reactions, which are controlled by reaction dynamics. Subtle modifications to the substrate or catalyst substituents alter the potential energy surface, leading to changes in predominant reaction pathways and altering the barriers to the major product when reaction dynamics are considered. In addition, this study suggests an explanation for the mysterious inversion of enantioselectivity resulting from the inclusion of an orthoiPrO group in the catalyst.

CONFPASS: Fast DFT Re-Optimizations of Structures from Conformation Searches

CONFPASS (Conformer Prioritizations and Analysis for DFT re-optimizations) has been developed to extract dihedral angle descriptors from conformational searching outputs, perform clustering, and return a priority list for density functional theory (DFT) re-optimizations. Evaluations were conducted with DFT data of the conformers for 150 structurally diverse molecules, most of which are flexible. CONFPASS gives a confidence estimate that the global minimum structure has been found, and based on our dataset, we can have 90% confidence after optimizing half of the FF structures. Re-optimizing conformers in order of the FF energy often generates duplicate results; using CONFPASS, the duplication rate is reduced by a factor of 2 for the first 30% of the re-optimizations, which include the global minimum structure about 80% of the time.

Selective Functionalisation of 5-Methylcytosine by Organic Photoredox Catalysis

The epigenetic modification 5-methylcytosine plays a vital role in development, cell specific gene expression and disease states. The selective chemical modification of the 5-methylcytosine methyl group is challenging. Currently, no such chemistry exists. Direct functionalisation of 5-methylcytosine would improve the detection and study of this epigenetic feature. We report a xanthone-photosensitised process that introduces a 4-pyridine modification at a C(sp3)-H bond in the methyl group of 5-methylcytosine. We propose a reaction mechanism for this type of reaction based on density functional calculations and apply transition state analysis to rationalise differences in observed reaction efficiencies between cyanopyridine derivatives. The reaction is initiated by single electron oxidation of 5-methylcytosine followed by deprotonation to generate the methyl group radical. Cross coupling of the methyl radical with 4-cyanopyridine installs a 4-pyridine label at 5-methylcytosine. We demonstrate use of the pyridination reaction to enrich 5-methylcytosine-containing ribonucleic acid.

Selective Functionalisation of 5-Methylcytosine by Organic Photoredox Catalysis

The epigenetic modification 5-methylcytosine plays a vital role in development, cell specific gene expression and disease states. The selective chemical modification of the 5-methylcytosine methyl group is challenging. Currently, no such chemistry exists. Direct functionalisation of 5-methylcytosine would improve the detection and study of this epigenetic feature. We report a xanthone-photosensitised process that introduces a 4-pyridine modification at a C(sp3 )-H bond in the methyl group of 5-methylcytosine. We propose a reaction mechanism for this type of reaction based on density functional calculations and apply transition state analysis to rationalise differences in observed reaction efficiencies between cyanopyridine derivatives. The reaction is initiated by single electron oxidation of 5-methylcytosine followed by deprotonation to generate the methyl group radical. Cross coupling of the methyl radical with 4-cyanopyridine installs a 4-pyridine label at 5-methylcytosine. We demonstrate use of the pyridination reaction to enrich 5-methylcytosine-containing ribonucleic acid.

Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm

Computational reaction prediction has become a ubiquitous task in chemistry due to the potential value accurate predictions can bring to chemists. Boronic acids are widely used in industry; however, understanding how to avoid the protodeboronation side reaction remains a challenge. We have developed an algorithm for in silico prediction of the rate of protodeboronation of boronic acids. A general mechanistic model devised through kinetic studies of protodeboronation was found in the literature and forms the foundation on which the algorithm presented in this work is built. Protodeboronation proceeds through 7 distinct pathways, though for any particular boronic acid, only a subset of mechanistic pathways are active. The rate of each active mechanistic pathway is linearly correlated with its characteristic energy difference, which in turn can be determined using Density Functional Theory. We validated the algorithm using leave-one-out cross-validation on a data set of 50 boronic acids and made a further 50 rate predictions on academically and industrially important boronic acids out of sample. We believe this work will provide great assistance to chemists performing reactions that feature boronic acids, such as Suzuki-Miyaura and Chan-Evans-Lam couplings.

Interpreting vibrational circular dichroism spectra: the Cai•factor for absolute configuration with confidence

Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs of small organic compounds and we use this database to validate a method for the automated analysis of VCD spectra and the assignment of absolute configuration: the Cai•factor (Configuration: absolute information). The analysis of the data demonstrates that the procedure is a reliable and time-efficient method for determination of absolute configuration, which gives both the assignment and a measure of confidence in the outcome, even when the spectra are imperfect. The majority of molecules tested have a high confidence score and all of these have the correct assignment.

MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations

The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol-1. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol-1 for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol-1.

Analysing a billion reactions with the RInChI

The RInChI is a canonical identifier for reactions which is widely used in reaction databases. It can be used to handle large collections of reactions and to link information from diverse data sources. How much information can it handle? Studies of the SAVI database, which contains more than a billion reactions, demonstrate that the RInChI is useful in analysing such a large collection of molecular data, and the reduced form of the Web-RInChIKey contains enough information to be an effective differentiator of reactions. Issues of NH tautomerism and stereochemistry are handled effectively. The RInChI illustrates that some of the properties of the algorithmically-generated SAVI database differ from SPRESI, which is a collection of experimental data. The RInChI has different properties to Reaction SMILES and both approaches provide useful and distinct information. We recommend that the RInChI be included in data models for reactions.

RSC CICAG Open Chemical Science meeting: integrating chemical data from two symposia and a series of workshops

In November 2020 the Royal Society of Chemistry Chemical Information and Computer Applications interest group (RSC CICAG) ran a five-day meeting entitled Open Chemical Science (https://www.rsc.org/events/detail/42090/open-chemical-science). This event had three intertwined themes, Open Data, Open Access publishing and a series of workshops highlighting a variety of Open-Source tools for chemistry. The online event proved to be enormously popular, with attendees from 45 different countries. The challenges involved in converting what was planned as a three-day physical event into a five day virtual event with three intertwined strands was recognised by the RSC with the award of the “2021 Inspirational Committee Award” (https://www.rsc.org/prizes-funding/prizes/2021-winners/rsc-chemical-information-and-computer-applications-group/). The workshops in particular proved to be enormously popular and spawned a year long series of further workshops.

The DP5 probability, quantification and visualisation of structural uncertainty in single molecules

Whenever a new molecule is made, a chemist will justify the proposed structure by analysing the NMR spectra. The widely-used DP4 algorithm will choose the best match from a series of possibilities, but draws no conclusions from a single candidate structure. Here we present the DP5 probability, a step-change in the quantification of molecular uncertainty: given one structure and one ¹³C NMR spectra, DP5 gives the probability of the structure being correct. We show the DP5 probability can rapidly differentiate between structure proposals indistinguishable by NMR to an expert chemist. We also show in a number of challenging examples the DP5 probability may prevent incorrect structures being published and later reassigned. DP5 will prove extremely valuable in fields such as discovery-driven automated chemical synthesis and drug development. Alongside the DP4-AI package, DP5 can help guide synthetic chemists when resolving the most subtle structural uncertainty. The DP5 system is available at https://github.com/Goodman-lab/DP5.

Correction: Computational insights on the origin of enantioselectivity in reactions with diarylprolinol silyl ether catalysts via a radical pathway

Correction for ‘Computational insights on the origin of enantioselectivity in reactions with diarylprolinol silyl ether catalysts via a radical pathway’ by Ching Ching Lam et al., Org. Chem. Front., 2022, https://doi.org/10.1039/d2qo00354f.

Computational insights on the origin of enantioselectivity in reactions with diarylprolinol silyl ether catalysts via a radical pathway

The stereoselective reaction of 1,4-dicarbonyls with diarylprolinol silyl ether catalysts was studied with force field and density functional theory calculations.

N-Triflylphosphoramides: highly acidic catalysts for asymmetric transformations

N-Triflylphosphoramides (NTPA), have become increasingly popular catalysts in the development of enantioselective transformations as they are stronger Brønsted acids than the corresponding phosphoric acids (PA). Their highly acidic, asymmetric active site can activate difficult, unreactive substrates. In this review, we present an account of asymmetric transformations using this type of catalyst that have been reported in the past ten years and we classify these reactions using the enantio-determining step as the key criterion. This compendium of NTPA-catalysed reactions is organised into the following categories: (1) cycloadditions, (2) electrocyclisations, polyene and related cyclisations, (3) addition reactions to imines, (4) electrophilic aromatic substitutions, (5) addition reactions to carbocations, (6) aldol and related reactions, (7) addition reactions to double bonds, and (8) rearrangements and desymmetrisations. We highlight the use of NTPA in total synthesis and suggest mnemonics which account for their enantioselectivity.

InChI version 1.06: now more than 99.99% reliable

The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier.

VRAI-selectivity: calculation of selectivity beyond transition state theory

In recent years, a growing number of organic reactions in the literature have shown selectivity controlled by reaction dynamics rather than by transition state theory. Such reactions are difficult to analyse because the transition state theory approach often does not capture the subtlety of the energy landscapes the compounds traverse and, therefore, cannot accurately predict the selectivity. We present an algorithm that can predict the major product and selectivity for a wide range of potential energy surfaces where the product distribution is influenced by reaction dynamics. The method requires as input calculation of the transition states, the intermediate (if present) and the product geometries. The algorithm is quick and simple to run and, except for two reactions with long alkyl chains, calculates selectivity more accurately than transition state theory alone.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Confidence in Inactive and Active Predictions from Structural Alerts

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

A Computational and Experimental Investigation of the Origin of Selectivity in the Chiral Phosphoric Acid Catalyzed Enantioselective Minisci Reaction

The Minisci reaction is one of the most valuable methods for directly functionalizing basic heteroarenes to form carbon–carbon bonds. Use of prochiral, heteroatom-substituted radicals results in stereocenters being formed adjacent to the heteroaromatic system, generating motifs which are valuable in medicinal chemistry and chiral ligand design. Recently a highly enantioselective and regioselective protocol for the Minisci reaction was developed, using chiral phosphoric acid catalysis. However, the precise mechanism by which this process operated and the origin of selectivity remained unclear, making it challenging to develop the reaction more generally. Herein we report further experimental mechanistic studies which feed into detailed DFT calculations that probe the precise nature of the stereochemistry-determining step. Computational and experimental evidence together support Curtin–Hammett control in this reaction, with initial radical addition being quick and reversible, and enantioselectivity being achieved in the subsequent slower, irreversible deprotonation. A detailed survey via DFT calculations assessed a number of different possibilities for selectivity-determining deprotonation of the radical cation intermediate. Computations point to a clear preference for an initially unexpected mode of internal deprotonation enacted by the amide group, which is a crucial structural feature of the radical precursor, with the assistance of the associated chiral phosphate. This unconventional stereodetermining step underpins the high enantioselectivities and regioselectivities observed. The mechanistic model was further validated by applying it to a test set of substrates possessing varied structural features.

Synergism of anisotropic and computational NMR methods reveals the likely configuration of phormidolide A

Characterization of the complex molecular scaffold of the marine polyketide natural product phormidolide A represents a challenge that has persisted for nearly two decades. In light of discordant results arising from recent synthetic and biosynthetic reports, a rigorous study of the configuration of phormidolide A was necessary. This report outlines a synergistic effort employing computational and anisotropic NMR investigation, that provided orthogonal confirmation of the reassigned side chain, as well as supporting a further correction of the C7 stereocenter.

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Charge-assisted phosph(v)azane anion receptors

Coordination of Cu(i) or Pd(ii) to seleno-cyclodiphosph(v)azanes of the type [RNH(Se)P(μ-NtBu)]2 results in positively charged anion receptor units which have increased anion affinity over the neutral seleno-phosph(v)azanes, due to the increase in electrostatic interactions between the receptor and the guest anions. The same effect is produced by replacement of one of the P[double bond, length as m-dash]Se units by a P-Me+ unit.

DP4-AI automated NMR data analysis: straight from spectrometer to structure

A robust system for automatic processing and assignment of raw 13C and 1H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated 13C and 1H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at https://github.com/KristapsE/DP4-AI.

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Using 2D Structural Alerts to Define Chemical Categories for Molecular Initiating Events

Molecular initiating events (MIEs) are important concepts for in silico predictions. They can be used to link chemical characteristics to biological activity through an adverse outcome pathway (AOP). In this work, we capture chemical characteristics in 2D structural alerts, which are then used as models to predict MIEs. An automated procedure has been used to identify these alerts, and the chemical categories they define have been used to provide quantitative predictions for the activity of molecules that contain them. This has been done across a diverse group of 39 important pharmacological human targets using open source data. The alerts for each target combine into a model for that target, and these models are joined into a tool for MIE prediction with high average model performance (sensitivity = 82%, specificity = 93%, overall quality = 93%, Matthews correlation coefficient = 0.57). The result is substantially improved from our previous study (Allen, T. E. H., Goodman, J. M., Gutsell, S., and Russell, P. J. 2016. A history of the molecular initiating event. Chem. Res. Toxicol. 29, 2060-2070) for which the mean sensitivity for each target was only 58%. This tool provides the first step in an AOP-based risk assessment, linking chemical structure to toxicity endpoint.

Prolonged P wave duration is associated with right atrial dimensions, but not atrial arrhythmias, in middle-aged endurance athletes

BACKGROUND

Atrial arrhythmias occur at a higher than expected prevalence amongst endurance athletes. Few studies have examined both atrial structure and arrhythmias in middle-aged endurance athletes. We examined the relationship between P-wave duration, atrial dimensions, and the presence of atrial ectopy in long-standing, middle-aged endurance athletes.

METHODS

Middle-aged athletes with a minimum of 10 years of competitive endurance sport history and no history of structural heart disease or clinical atrial arrhythmias, had 12-lead ECGs to assess P-wave duration, signal-averaged ECGs (SAECG) to assess filtered P-wave duration, a 24 h Holter monitor to assess atrial ectopy, and echocardiography and cardiac magnetic resonance imaging to assess atrial structural characteristics.

RESULTS

Amongst endurance athletes (n = 104; mean age = 54 ± 5 years; 63% male), filtered P-wave duration on SAECG was correlated with P-wave duration on 12-lead ECG (r = 0.36, p, 0.0001), as well as with larger CMR-derived RA areas (r = 0.30, p = 0.01) and volumes (r = 0.24, p < 0.05). There was no correlation between filtered P-wave duration and any LA measures on imaging (p > 0.05). There was no correlation between the incidence of atrial ectopy (premature atrial contractions or atrial tachycardia) and any electrocardiographic or structural measures.

CONCLUSION

Longer filtered P-wave duration was associated with larger RA areas and volumes, without an increase in atrial ectopy.

BINOPtimal: a web tool for optimal chiral phosphoric acid catalyst selection

A catalyst selection program, BINOPtimal, has been developed.

The optimal DFT approach in DP4 NMR structure analysis – pushing the limits of relative configuration elucidation

What computational methods should be used to achieve the most reliable result in computational structure elucidation? A study on the effect of quality and quantity of geometries on computational NMR structure elucidation performance is reported.

Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,β-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

International chemical identifier for reactions (RInChI)

The Reaction InChI (RInChI) extends the idea of the InChI, which provides a unique descriptor of molecular structures, towards reactions. Prototype versions of the RInChI have been available since 2011. The first official release (RInChI-V1.00), funded by the InChI Trust, is now available for download (http://www.inchi-trust.org/downloads/). This release defines the format and generates hashed representations (RInChIKeys) suitable for database and web operations. The RInChI provides a concise description of the key data in chemical processes, and facilitates the manipulation and analysis of reaction data.

A synthesis-enabled relative stereochemical assignment of the C1-C28 region of hemicalide

Through synthesising both candidate diastereomers of a model C1-C28 fragment of the potent cytotoxic marine polyketide hemicalide, an assignment of the relative configuration between the C1-C15 and C16-C26 regions has been achieved. By detailed NMR comparisons with the natural product, the relative stereochemistry between these two 1,6-related stereoclusters is elucidated as 13,18-syn rather than the previously proposed 13,18-anti relationship. A flexible and modular strategy using an advanced C1-C28 ketone fragment 22 is outlined to elucidate the remaining stereochemical features and achieve a total synthesis.

Selecting Chiral BINOL-Derived Phosphoric Acid Catalysts: General Model To Identify Steric Features Essential for Enantioselectivity

Choosing the optimal catalyst for a new transformation is challenging because the ideal molecular requirements of the catalyst for one reaction do not always simply translate to another. Large groups at the 3,3' positions of the binaphthol rings are important for efficient stereoinduction but if they are too large this can lead to unusual or poor results. By applying a quantitative steric assessment of the substituents at the 3,3' positions of the binaphthol ring, we have systematically studied the effect of modulating this group on enantioselectivity for a wide range of reactions involving imines, and verified this analysis using ONIOM calculations. We have shown that in most reactions, the stereochemical outcome depends on both proximal and remote sterics. Summarising detailed calculations into a simple qualitative model identifies and explains the steric features required for high selectivity. This model is consistent with seventy seven papers reporting reactions (over 1000 transformations in total), and provides a straightforward decision tree for selecting the best catalyst.

A possible extension to the RInChI as a means of providing machine readable process data

The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed ProcAuxInfo, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the ProcAuxInfo layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents.

(Z)-Selective Takai olefination of salicylaldehydes

The Takai olefination (or Takai reaction) is a method for the conversion of aldehydes to vinyl iodides, and has seen widespread implementation in organic synthesis. The reaction is usually noted for its high (E)-selectivity; however, herein we report the highly (Z)-selective Takai olefination of salicylaldehyde derivatives. Systematic screening of related substrates led to the identification of key factors responsible for this surprising inversion of selectivity, and enabled the development of a modified mechanistic model to rationalise these observations.

Transfer hydrogenation of ortho-hydroxybenzophenone ketimines catalysed by BINOL-derived phosphoric acid occurs by a 14-membered bifunctional transition structure

Chiral BINOL-derived phosphoric acids catalyse the transfer hydrogenation of ketimines using Hantszch esters.

A History of the Molecular Initiating Event

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Goldilocks Catalysts: Computational Insights into the Role of the 3,3' Substituents on the Selectivity of BINOL-Derived Phosphoric Acid Catalysts

BINOL-derived phosphoric acids provide effective asymmetric catalysis for many organic reactions. Catalysts based on this scaffold show a large structural diversity, especially in the 3,3' substituents, and little is known about the molecular requirements for high selectivity. As a result, selection of the best catalyst for a particular transformation requires a trial and error screening process, as the size of the 3,3' substituents is not simply related to their efficacy: the right choice is neither too large nor too small. We have developed an approach to identify and quantify structural features on the catalyst that determine selectivity. We show that the application of quantitative steric parameters (a new measure, AREA(θ), and rotation barrier) to an imine hydrogenation reaction allows the identification of catalyst features necessary for efficient stereoinduction, validated by QM/MM hybrid calculations.

A Practical Guide for Predicting the Stereochemistry of Bifunctional Phosphoric Acid Catalyzed Reactions of Imines

Chiral phosphoric acids have become powerful catalysts for the stereocontrolled synthesis of a diverse array of organic compounds. Since the initial report, the development of phosphoric acids as catalysts has been rapid, demonstrating the tremendous generality of this catalyst system and advancing the use of phosphoric acids to catalyze a broad range of asymmetric transformations ranging from Mannich reactions to hydrogenations through complementary modes of activation. These powerful applications have been developed without a clear mechanistic understanding of the reasons for the high level of stereocontrol. This Account describes investigations into the mechanism of the phosphoric acid catalyzed addition of nucleophiles to imines, focusing on binaphthol-based systems. In many cases, the hydroxyl phosphoric acid can form a hydrogen bond to the imine while the P═O interacts with the nucleophile. The single catalyst, therefore, activates both the electrophile and the nucleophile, while holding both in the chiral pocket created by the binaphthol and constrained by substituents at the 3 and 3' positions. Detailed geometric and energetic information about the transition states can be gained from calculations using ONIOM methods that combine the advantages of DFT with some of the speed of force fields. These high-level calculations give a quantitative account of the selectivity in many cases, but require substantial computational resources. A simple qualitative model is a useful complement to this complex quantitative model. We summarize our calculations into a working model that can readily be sketched by hand and used to work out the likely sense of selectivity for each reaction. The steric demands of the different parts of the reactants determine how they fit into the chiral cavity and which of the competing pathways is favored. The preferred pathway can be found by considering the size of the substituents on the nitrogen and carbon atoms of the imine electrophile, and the position of the nucleophilic site on the nucleophile in relation to the hydrogen-bond which holds it in the catalyst active site. We present a guide to defining the pathway in operation allowing the fast and easy prediction of the stereochemical outcome and provide an overview of the breadth of reactions that can be explained by these models including the latest examples.