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].

Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.

C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes: product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks.

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).

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.

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.

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.

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.

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.

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.

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.

Rapid Route-Finding for Bifurcating Organic Reactions.

A large number of organic reactions feature post-transition-state bifurcations. Selectivities in such reactions are difficult to analyze because they cannot be determined by comparing the energies of competing transition states. Molecular dynamics approaches can provide answers but are computationally very expensive. We present an algorithm that predicts the major products in bifurcating organic reactions with negligible computational cost. The method requires two transition states, two product geometries, and no additional information. The algorithm correctly predicts the major product for about 90% of the organic reactions investigated. For the remaining 10% of the reactions, the algorithm returns a warning indication that the conclusion may be uncertain. The method also reproduces the experimental and the molecular dynamics product ratios within 15% for more than 80% of the reactions. We have successfully applied the method to a trifurcating organic reaction, a carbocation rearrangement, and solvent-dependent Pummerer-like reactions, demonstrating the power of the algorithm to simplify and to help understand highly complex reactions.

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.

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.

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.

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.

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.

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. Semi-empirical, HF and DFT methods were explored, and B3LYP optimized geometries in combination with mPW1PW91 shifts and M06-2X conformer energies was found to be best. The required number of conformers considered has also been investigated, as well as several methods for the reduction of this number. Clear guidelines for the best computational NMR structure elucidation methods for different levels of available computing power are provided.

Guest Binding via N-H⋠⋠⋠π Bonding and Kinetic Entrapment by an Inorganic Macrocycle.

Modern supramolecular chemistry is overwhelmingly based on non-covalent interactions involving organic architectures. However, the question of what happens when you depart from this area to the supramolecular chemistry of structures based on non-carbon frameworks remains largely unanswered, and is an area that potentially provides new directions in molecular activation, host-guest chemistry, and biomimetic chemistry. In this work, we explore the unusual host-guest chemistry of the pentameric macrocycle [{P(µ-Nt Bu}2 NH]5 with a range of anionic and neutral guests. The polar coordination site of this host promotes new modes of guest encapsulation via hydrogen bonding with the π systems of the unsaturated C≡C and C≡N bonds of acetylenes and nitriles as well as with the PCO- anion. Halide guests can be kinetically locked within the structure by oxidation of the phosphorus periphery by oxidation to PV . Our study underscores the future promise of p-block macrocyclic chemistry.

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,ß-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.

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.