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).

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.

Shibboleth: An agent-based model of signalling mimicry.

Mimicry is an essential strategy for exploiting competitors in competitive co-evolutionary relationships. Protection against mimicry may, furthermore, be a driving force in human linguistic diversity: the potential harm caused by failing to detect mimicked group-identity signals may select for high sensitivity to mimicry of honest group members. Here we describe the results of five agent-based models that simulate multi-generational interactions between two groups of individuals: original members of a group with an honest identity signal, and members of an outsider group who mimic that signal, aiming to pass as members of the in-group. The models correspond to the Biblical story of Shibboleth, where a tribe in conflict with another determines tribe affiliation by asking individuals to pronounce the word, 'Shibboleth.' In the story, failure to reproduce the word phonetically resulted in death. Here, we run five different versions of a 'Shibboleth' model: a first, simple version, which evaluates whether a composite variable of mimicry quality and detection quality is a superior predictor to the model's outcome than is cost of detection. The models thereafter evaluate variations on the simple model, incorporating group-level behaviours such as altruistic punishment. Our results suggest that group members' sensitivity to mimicry of the Shibboleth-signal is a better predictor of whether any signal of group identity goes into fixation in the overall population than is the cost of mimicry detection. Thus, the likelihood of being detected as a mimic may be more important than the costs imposed on mimics who are detected. This suggests that theoretical models in biology should place greater emphasis on the likelihood of detection, which does not explicitly entail costs, rather than on the costs to individuals who are detected. From a language learning perspective, the results suggest that admission to group membership through linguistic signals is powered by the ability to imitate and evade detection as an outsider by existing group members.

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.

Overcoming your inner goblin.

A rise in antisocial behaviour indicates covid-19 lockdowns disrupted our cultural evolution, says Jonathan R. Goodman.

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.

Preclinical evaluation of a third-generation absorbable antibacterial envelope.

BACKGROUND: The TYRX (Medtronic) absorbable antibacterial envelope has been shown to stabilize implantable cardiac devices and reduce infection. A third-generation envelope was developed to reduce surface roughness with a redesigned multifilament mesh and enhanced form factor but identical polymer coating and antibiotic concentrations as the currently available second-generation envelope. OBJECTIVE: The purpose of this study was to compare drug elution, bacterial challenge efficacy, stabilization, and absorption of second- vs third-generation envelopes. METHODS: Antibiotic elution was assessed in vitro and in vivo. For efficacy against gram-positive/gram-negative bacteria, 40 rabbits underwent device insertions with or without third-generation envelopes. For stabilization (migration, rotation), 5 sheep were implanted with 6 devices each in second- or third-generation envelopes. Prespecified acceptance criteria were <83-mm migration and <90° rotation. Absorption was assessed via gross pathology. RESULTS: Elution curves were equivalent (similarity factors ≥50 per Food and Drug Administration guidance). Third-generation envelopes eluted antibiotics above minimal inhibitory concentration (MIC) in vivo at 2 hours postimplant through 7 days, consistent with second-generation envelopes. Bacterial challenge showed reductions (P <.05) in infection with second- and third-generation envelopes. Device migration was 5.5 ± 3.5 mm (third-generation) vs 9. 9 ±7.9 mm (second-generation) (P <.05). Device rotation was 18.9° ± 11.4° (third-generation) vs 17.6° ± 15.1° (second-generation) and did not differ (P = .79). Gross pathology confirmed the absence of luminal mesh remainders and no differences in peridevice fibrosis at 9 or 12 weeks. CONCLUSION: The third-generation TYRX absorbable antibacterial envelope demonstrated equivalent preclinical performance to the second-generation envelope. Antibiotic elution curves were similar, elution was above MIC for 7 days, infections were reduced compared to no envelope, and acceptance criteria for migration, rotation, and absorption were met.

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.

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.

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.

Inconsistencies in mapping current distribution in transcranial direct current stimulation.

Introduction: tDCS is a non-invasive neuromodulation technique that has been widely studied both as a therapy for neuropsychiatric diseases and for cognitive enhancement. However, recent meta-analyses have reported significant inconsistencies amongst tDCS studies. Enhancing empirical understanding of current flow in the brain may help elucidate some of these inconsistencies. Methods: We investigated tDCS-induced current distribution by injecting a low frequency current waveform in a phantom and in vivo. MR phase images were collected during the stimulation and a time-series analysis was used to reconstruct the magnetic field. A current distribution map was derived from the field map using Ampere's law. Results: The current distribution map in the phantom showed a clear path of current flow between the two electrodes, with more than 75% of the injected current accounted for. However, in brain, the results did evidence a current path between the two target electrodes but only some portion ( 25%) of injected current reached the cortex demonstrating that a significant fraction of the current is bypassing the brain and traveling from one electrode to the other external to the brain, probably due to conductivity differences in brain tissue types. Substantial inter-subject and intra-subject (across consecutive scans) variability in current distribution maps were also observed in human but not in phantom scans. Discussions: An in-vivo current mapping technique proposed in this study demonstrated that much of the injected current in tDCS was not accounted for in human brain and deviated to the edge of the brain. These findings would have ramifications in the use of tDCS as a neuromodulator and may help explain some of the inconsistencies reported in other studies.

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.

The evolution of barriers to exploitation: Sometimes the Red Queen can take a break.

We propose a general barrier theory as an evolutionary framework for understanding coevolutionary effects of conflicts of interest in natural and human systems. It is generalized from the barrier theory of cancer, which describes how cancer develops through the evasion of mechanisms, that block unregulated cellular reproduction and survival. Barriers are naturally evolved or artificially implemented mechanisms for blocking exploitation; restraints are mechanisms that impede but do not block exploitation. When conflicts of interest arise, selection will favor exploiters that are capable of overcoming barriers and restraints. When barriers are in place, they halt, at least temporarily, coevolutionary arms races (the Red Queen can stop running). Barriers occur in a broad spectrum of interactions characterized by conflicts of interest: barriers to cellular survival (apoptosis) and reproduction (cell cycle arrest) may block a virus from replicating its genome through reproduction of its host cell. Vaccines may completely protect against targeted pathogens. A plant may escape herbivory by evolving defensive chemicals that block herbivory. Obligate mutualisms may evolve when barriers to horizontal transmission favor symbionts that increasingly lose mechanisms that contribute to horizontal transmission. Here, we show how the barrier theory applies across a spectrum of natural and social systems.