Trapping of Nicotinic Acetylcholine Receptor Ligands Assayed by In Vitro Cellular Studies and In Vivo PET Imaging.

A question relevant to nicotine addiction is how nicotine and other nicotinic receptor membrane-permeant ligands, such as the anti-smoking drug varenicline (Chantix), distribute in brain. Ligands, like varenicline, with high pKa and high affinity for α4ß2-type nicotinic receptors (α4ß2Rs) are trapped in intracellular acidic vesicles containing α4ß2Rs in vitro Nicotine, with lower pKa and α4ß2R affinity, is not trapped. Here, we extend our results by imaging nicotinic PET ligands in vivo in male and female mouse brain and identifying the trapping brain organelle in vitro as Golgi satellites (GSats). Two PET 18F-labeled imaging ligands were chosen: [18F]2-FA85380 (2-FA) with varenicline-like pKa and affinity and [18F]Nifene with nicotine-like pKa and affinity. [18F]2-FA PET-imaging kinetics were very slow consistent with 2-FA trapping in α4ß2R-containing GSats. In contrast, [18F]Nifene kinetics were rapid, consistent with its binding to α4ß2Rs but no trapping. Specific [18F]2-FA and [18F]Nifene signals were eliminated in ß2 subunit knock-out (KO) mice or by acute nicotine (AN) injections demonstrating binding to sites on ß2-containing receptors. Chloroquine (CQ), which dissipates GSat pH gradients, reduced [18F]2-FA distributions while having little effect on [18F]Nifene distributions in vivo consistent with only [18F]2-FA trapping in GSats. These results are further supported by in vitro findings where dissipation of GSat pH gradients blocks 2-FA trapping in GSats without affecting Nifene. By combining in vitro and in vivo imaging, we mapped both the brain-wide and subcellular distributions of weak-base nicotinic receptor ligands. We conclude that ligands, such as varenicline, are trapped in neurons in α4ß2R-containing GSats, which results in very slow release long after nicotine is gone after smoking.SIGNIFICANCE STATEMENT Mechanisms of nicotine addiction remain poorly understood. An earlier study using in vitro methods found that the anti-smoking nicotinic ligand, varenicline (Chantix) was trapped in α4ß2R-containing acidic vesicles. Using a fluorescent-labeled high-affinity nicotinic ligand, this study provided evidence that these intracellular acidic vesicles were α4ß2R-containing Golgi satellites (GSats). In vivo PET imaging with F-18-labeled nicotinic ligands provided additional evidence that differences in PET ligand trapping in acidic vesicles were the cause of differences in PET ligand kinetics and subcellular distributions. These findings combining in vitro and in vivo imaging revealed new mechanistic insights into the kinetics of weak base PET imaging ligands and the subcellular mechanisms underlying nicotine addiction.

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

10-Year Cumulative Incidence and Indications for Revision Total Joint Arthroplasty for Patients Who Have Ehlers-Danlos syndrome.

BACKGROUND: Long-term complications following total joint arthroplasty are not well established for patients who have Ehlers-Danlos syndrome (EDS), a group of connective tissue disorders. This study compared 10-year incidence of revision surgery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in patients who have and do not have EDS. METHODS: A retrospective cohort analysis was conducted using a national all-payer claims database from 2010 to 2021 to identify patients who underwent primary TKA or THA. Patients who had and did not have EDS were propensity-score matched by age, sex, and a comorbidity index. Kaplan-Meier analyses and Cox proportional hazard models were utilized to determine the cumulative incidence and risks of revision experienced by patients who have and do not have EDS. RESULTS: The EDS patients who underwent TKA had a higher risk of all-cause revision (hazard ratio (HR): 1.50, 95% confidence interval (95% CI): 1.09 to 2.07, P < 0.014) and risk of revision due to instability (HR = 2.49, 95% CI: 1.37 to 4.52, P < 0.003). The EDS patients who underwent THA had a higher risk of all-cause revision (HR = 2.32, 95% CI: 1.47 to 3.65, P < 0.001), revision due to instability (HR = 4.26, 95% CI: 2.17 to 8.36, P < 0.001), and mechanical loosening (HR = 3.63, 95% CI: 2.05 to 6.44, P < 0.001). CONCLUSION: Patients who had EDS were found to have a higher incidence of revision within 10 years of undergoing TKA and THA compared to matched controls, especially for instability. Patients who have EDS should be counseled accordingly. Surgical technique and implant selection should include consideration for increased constraint in TKA and larger femoral heads or dual mobility articulations for THA.

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.

Characterizing Uncertainty in Machine Learning for Chemistry.

Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on data sets of molecular properties, we show important trends in model performance associated with the level of noise in the data set, size of the data set, model architecture, molecule representation, ensemble size, and data set splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, and 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts.

Computing Kinetic Solvent Effects and Liquid Phase Rate Constants Using Quantum Chemistry and COSMO-RS Methods.

Many industrially and environmentally relevant reactions occur in the liquid phase. An accurate prediction of the rate constants is needed to analyze the intricate kinetic mechanisms of condensed phase systems. Quantum chemistry and continuum solvation models are commonly used to compute liquid phase rate constants; yet, their exact computational errors remain largely unknown, and a consistent computational workflow has not been well established. In this study, the accuracies of various quantum chemical and COSMO-RS levels of theory are assessed for the predictions of liquid phase rate constants and kinetic solvent effects. The prediction is made by first obtaining gas phase rate constants and subsequently applying solvation corrections. The calculation errors are evaluated using the experimental data of 191 rate constants that comprise 15 neutral closed-shell or free radical reactions and 49 solvents. The ωB97XD/def2-TZVP level of theory combined with the COSMO-RS method at the BP-TZVP level is shown to achieve the best performance with a mean absolute error of 0.90 in log10(kliq). Relative rate constants are additionally compared to determine the errors associated with the solvation calculations alone. Very accurate predictions of relative rate constants are achieved at nearly all levels of theory with a mean absolute error of 0.27 in log10(ksolvent1/ksolvent2).

Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes.

Although charged solutes are common in many chemical systems, traditional solvation models perform poorly in calculating solvation energies of ions. One major obstacle is the scarcity of experimental data for solvated ions. In this study, we release an experiment-based aqueous ionic solvation energy data set, IonSolv-Aq, that contains hydration free energies for 118 anions and 155 cations, more than 2 times larger than the set of hydration free energies for singly charged ions contained in the 2012 Minnesota Solvation Database commonly used in benchmarking studies. We discuss sources of systematic uncertainty in the data set and use the data to examine the accuracy of popular implicit solvation models COSMO-RS and SMD for predicting solvation free energies of singly charged ionic solutes in water. Our results indicate that most SMD and COSMO-RS modeling errors for ionic solutes are systematic and correctable with empirical parameters. We discuss two systematic offsets: one across all ions and one that depends on the functional group of the ionization site. After correcting for these offsets, solvation energies of singly charged ions are predicted using COSMO-RS to 3.1 kcal mol-1 MAE against a challenging test set and 1.7 kcal mol-1 MAE (about 3% relative error) with a filtered test set. The performance of SMD is similar, with MAE against those same test sets of 2.7 and 1.7 kcal mol-1. These results underscore the importance of compiling larger experimental data sets to improve solvation model parametrization and fairly assess performance.

Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments.

The combustion and pyrolysis behaviors of light esters and fatty acid methyl esters have been widely studied due to their relevance as biofuel and fuel additives. However, a knowledge gap exists for midsize alkyl acetates, especially ones with long alkoxyl groups. Butyl acetate, in particular, is a promising biofuel with its economic and robust production possibilities and ability to enhance blendstock performance and reduce soot formation. However, it is little studied from both experimental and modeling aspects. This work created detailed oxidation mechanisms for the four butyl acetate isomers (normal-, sec-, tert-, and iso-butyl acetate) at temperatures varying from 650 to 2000 K and pressures up to 100 atm using the Reaction Mechanism Generator. About 60% of species in each model have thermochemical parameters from published data or in-house quantum calculations, including fuel molecules and intermediate combustion products. Kinetics of essential primary reactions, retro-ene and hydrogen atom abstraction by OH or HO2, governing the fuel oxidation pathways, were also calculated quantum-mechanically. Simulation of the developed mechanisms indicates that the majority of the fuel will decompose into acetic acid and relevant butenes at elevated temperatures, making their ignition behaviors similar to butenes. The adaptability of the developed models to high-temperature pyrolysis systems was tested against newly collected high-pressure shock experiments; the simulated CO mole fraction time histories have a reasonable agreement with the laser measurement in the shock tube. This work reveals the high-temperature oxidation chemistry of butyl acetates and demonstrates the validity of predictive models for biofuel chemistry established on accurate thermochemical and kinetic parameters.

On the accuracy of the chemically significant eigenvalue method.

We study the accuracy and convergence properties of the chemically significant eigenvalues method as proposed by Georgievskii et al. [J. Phys. Chem. A 117, 12146-12154 (2013)] and its close relative, dominant subspace truncation, for reduction of the energy-grained master equation. We formally derive the connection between both reduction techniques and provide hard error bounds for the accuracy of the latter which confirm the empirically excellent accuracy and convergence properties but also unveil practically relevant cases in which both methods are bound to fall short. We propose the use of balanced truncation as an effective alternative in these cases.

Assault-related anoxia and neck injuries in US emergency departments.

BACKGROUND: Early identification of non-fatal strangulation in the context of intimate partner violence (IPV) is crucial due to its severe physical and psychological consequences for the individual experiencing it. This study investigates the under-reported and underestimated burden of IPV-related non-fatal strangulation by analysing assault-related injuries leading to anoxia and neck injuries. METHODS: An IRB-exempt, retrospective review of prospectively collected data were performed using the National Electronic Injury Surveillance System All Injury Programme data from 2005 to 2019 for all assaults resulting in anoxia and neck injuries. The type and mechanism of assault injuries resulting in anoxia (excluding drowning, poisoning and aspiration), anatomical location of assault-related neck injuries and neck injury diagnosis by morphology, were analysed using statistical methods accounting for the weighted stratified nature of the data. RESULTS: Out of a total of 24 493 518 assault-related injuries, 11.6% (N=2 842 862) resulted from IPV (defined as perpetrators being spouses/partners). Among 22 764 cases of assault-related anoxia, IPV accounted for 40.4%. Inhalation and suffocation were the dominant mechanisms (60.8%) of anoxia, with IPV contributing to 41.9% of such cases. Neck injuries represented only 3.0% of all assault-related injuries, with IPV accounting for 21% of all neck injuries and 31.9% of neck contusions. CONCLUSIONS: The study reveals a significant burden of IPV-related anoxia and neck injuries, highlighting the importance of recognising IPV-related strangulation. Comprehensive screening for IPV should be conducted in patients with unexplained neck injuries, and all IPV patients should be screened for strangulation events.

Evaluation of an Image-Derived Input Function for Kinetic Modeling of Nicotinic Acetylcholine Receptor-Binding PET Ligands in Mice.

Positron emission tomography (PET) radioligands that bind with high-affinity to α4ß2-type nicotinic receptors (α4ß2Rs) allow for in vivo investigations of the mechanisms underlying nicotine addiction and smoking cessation. Here, we investigate the use of an image-derived arterial input function and the cerebellum for kinetic analysis of radioligand binding in mice. Two radioligands were explored: 2-[18F]FA85380 (2-FA), displaying similar pKa and binding affinity to the smoking cessation drug varenicline (Chantix), and [18F]Nifene, displaying similar pKa and binding affinity to nicotine. Time-activity curves of the left ventricle of the heart displayed similar distribution across wild type mice, mice lacking the ß2-subunit for ligand binding, and acute nicotine-treated mice, whereas reference tissue binding displayed high variation between groups. Binding potential estimated from a two-tissue compartment model fit of the data with the image-derived input function were higher than estimates from reference tissue-based estimations. Rate constants of radioligand dissociation were very slow for 2-FA and very fast for Nifene. We conclude that using an image-derived input function for kinetic modeling of nicotinic PET ligands provides suitable results compared to reference tissue-based methods and that the chemical properties of 2-FA and Nifene are suitable to study receptor response to nicotine addiction and smoking cessation therapies.

Inflammation and Metabolism of Influenza-Stimulated Peripheral Blood Mononuclear Cells From Adults With Obesity Following Bariatric Surgery.

BACKGROUND: Obesity dysregulates immunity to influenza infection. Therefore, there is a critical need to investigate how obesity impairs immunity and to establish therapeutic approaches that mitigate the impact of increased adiposity. One mechanism by which obesity may alter immune responses is through changes in cellular metabolism. METHODS: We studied inflammation and cellular metabolism of peripheral blood mononuclear cells (PBMCs) isolated from individuals with obesity relative to lean controls. We also investigated if impairments to PBMC metabolism were reversible upon short-term weight loss following bariatric surgery. RESULTS: Obesity was associated with systemic inflammation and poor inflammation resolution. Unstimulated PBMCs from participants with obesity had lower oxidative metabolism and adenosine triphosphate (ATP) production compared to PBMCs from lean controls. PBMC secretome analyses showed that ex vivo stimulation with A/Cal/7/2009 H1N1 influenza led to a notable increase in IL-6 with obesity. Short-term weight loss via bariatric surgery improved biomarkers of systemic metabolism but did not improve markers of inflammation resolution, PBMC metabolism, or the PBMC secretome. CONCLUSIONS: These results show that obesity drives a signature of impaired PBMC metabolism, which may be due to persistent inflammation. PBMC metabolism was not reversed after short-term weight loss despite improvements in measures of systemic metabolism.

Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures.

The solubility of organic molecules is crucial in organic synthesis and industrial chemistry; it is important in the design of many phase separation and purification units, and it controls the migration of many species into the environment. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here, we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5000 experimental solid solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550 K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute's sublimation enthalpy, are also presented.

Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing.

Gauging the chemical stability of active pharmaceutical ingredients (APIs) is critical at various stages of pharmaceutical development to identify potential risks from drug degradation and ensure the quality and safety of the drug product. Stress testing has been the major experimental method to study API stability, but this analytical approach is time-consuming, resource-intensive, and limited by API availability, especially during the early stages of drug development. Novel computational chemistry methods may assist in screening for API chemical stability prior to synthesis and augment contemporary API stress testing studies, with the potential to significantly accelerate drug development and reduce costs. In this work, we leverage quantum chemical calculations and automated reaction mechanism generation to provide new insights into API degradation studies. In the continuation of part one in this series of studies [Grinberg Dana et al., Mol. Pharm. 2021 18 (8), 3037-3049], we have generated the first ab initio predictive chemical kinetic model of free-radical oxidative degradation for API stress testing. We focused on imipramine oxidation in an azobis(isobutyronitrile) (AIBN)/H2O/CH3OH solution and compared the model's predictions with concurrent experimental observations. We analytically determined iminodibenzyl and desimipramine as imipramine's two major degradation products under industry-standard AIBN stress testing conditions, and our ab initio kinetic model successfully identified both of them in its prediction for the top three degradation products. This work shows the potential and utility of predictive chemical kinetic modeling and quantum chemical computations to elucidate API chemical stability issues. Further, we envision an automated digital workflow that integrates first-principle models with data-driven methods that, when actively and iteratively combined with high-throughput experiments, can substantially accelerate and transform future API chemical stability studies.

Concluding remarks: Faraday Discussion on unimolecular reactions.

This Faraday Discussion, marking the centenary of Lindemann's explanation of the pressure-dependence of unimolecular reactions, presented recent advances in measuring and computing collisional energy transfer efficiencies, microcanonical rate coefficients, and pressure-dependent (phenomenological) rate coefficients, and the incorporation of these rate coefficients in kinetic models. Several of the presentations featured systems where breakdown of the Born-Oppenheimer approximation is key to understanding the measured rates/products. Many of the reaction systems presented were quite complex, which can make it difficult to go from "plausible proposed explanation" to "quantitative agreement between model and experiment". This complexity highlights the need for better automation of the calculations, better documentation and benchmarking to catch any errors and to make the calculations more easily reproducible, and continued (and even closer) cooperation of experimentalists and modelers. In some situations the correct definition of a "species" is debatable, since the population distributions and time evolution are so distorted from the perfect-Boltzmann Lewis-structure zero-order concept of a chemical species. Despite all these challenges, the field has made tremendous advances, and several cases were presented which demonstrated both excellent understanding of very complicated reaction chemistry and quantitatively accurate predictions of complicated experiments. Some of the interesting contributions to this Discussion are highlighted here, with some comments and suggestions for next steps.

Examining the accuracy of methods for obtaining pressure dependent rate coefficients.

The full energy-grained master equation (ME) is too large to be conveniently used in kinetic modeling, so almost always it is replaced by a reduced model using phenomenological rate coefficients. The accuracy of several methods for obtaining these pressure-dependent phenomenological rate coefficients, and so for constructing a reduced model, is tested against direct numerical solutions of the full ME, and the deviations are sometimes quite large. An algebraic expression for the error between the popular chemically-significant eigenvalue (CSE) method and the exact ME solution is derived. An alternative way to compute phenomenological rate coefficients, simulation least-squares (SLS), is presented. SLS is often about as accurate as CSE, and sometimes has significant advantages over CSE. One particular variant of SLS, using the matrix exponential, is as fast as CSE, and seems to be more robust. However, all of the existing methods for constructing reduced models to approximate the ME, including CSE and SLS, are inaccurate under some conditions, and sometimes they fail dramatically due to numerical problems. The challenge of constructing useful reduced models that more reliably emulate the full ME solution is discussed.

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task-dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions, and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields, or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark data sets for future work. All data sets and the developed reaction GCNN model are available online, free of charge, and open source.

Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications.

Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large for manual curation, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization, and exclusivity on the performance of different template ranking models. We find that duplicate and nonexclusive templates, i.e., templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of nonexclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved considerably for both heuristic and machine learning template ranking models, as well as multistep retrosynthetic planning models. The canonicalization and correction code is made freely available.