Discovery Of Reactions Research Articles

Photoactivated Formal [3 + 2]/[4+2] Cycloaddition of N‐Aryl Cyclopropyl and Cyclobutylamines

AbstractOrganic transformations initiated by photochemical activation have been at the forefront of reaction discovery. In this study, we present a formal photochemical [3+2] and [4+2] cycloaddition using N‐aryl cyclopropylamines and N‐aryl cyclobutylamines in conjunction with α,β‐unsaturated carbonyl systems, unveiling two distinct mechanistic pathways. The [3+2] cycloaddition is elucidated as being guided by the photochemical activity of an electron donor‐acceptor (EDA) complex. Simultaneously, intermolecular [4+2] annulation of cyclobutylanilines is achieved by visible‐light photoredox catalysis. These simple methodologies have wide applicability, facilitating the synthesis of N‐arylaminocycloalkyl compounds in yields ranging from good to excellent.

Accelerated Mechanochemical Bond Scission and Stabilization against Heat and Light in Carbamoyloxime Mechanophores.

Current approaches to the discovery of mechanochemical reactions in polymers are limited by the interconnection of the zero-force and force-modified potential energy surfaces since most mechanochemical reactions are force-biased thermal reactions. Here, carbamoyloximes are developed as a mechanophore class in which the mechanochemical reaction rates counterintuitively increase together with the thermal stability. All carbamoyloxime mechanophores undergo force-induced homolytic bond scission at the N-O bond, and their mechanochemical scission rate increases with the degree of substitution on the α-substituent. Yet, carbamoylaldoximes react to both heat and light with a pericyclic syn elimination, while carbamoylketoximes undergo thermal decomposition at high temperature and photochemical homolytic scission only from the triplet state. Thereby, the mechanochemical and thermal reaction trajectories are separated, and the thermal stability increases alongside the mechanochemical reaction kinetics. This approach may play an important role in the future of systematic mechanochemical reaction discovery.

Parallel and High Throughput Reaction Monitoring with Computer Vision

AbstractWe report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well‐plate high throughput settings, are described. Analysis of parallel dye‐quenching hydroxylations, DMAP‐catalysed esterification, solid‐liquid sedimentation dynamics, metal catalyst degradation, and biologically‐relevant sugar‐mediated nitro reduction reactions have each provided insight into the scope and limitations of camera‐enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi‐reaction time‐resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation.

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

High‐Throughput Experimentation (HTE) Applied to the Synthesis of API Impurities: A Case Study in Oxidative Decarboxylation of Aryl Acetic Acids

AbstractHigh‐throughput experimentation (HTE) has dramatically impacted experimental reaction development by enabling the rapid exploration of a diverse set of reaction conditions. During the past few decades, HTE has evolved as a tool to expedite reaction discovery and optimization. This work details the application of HTE to synthesize impurities of active pharmaceutical ingredients (APIs) with ketone/aldehyde functionality, specially focusing on ibuprofen impurity E. Initial experiments using K2S2O8 as oxidant yielded moderate results. Subsequent HTE screens identified cerium ammonium nitrate (CAN) and RuCl3‐NaIO4 as new effective decarboxylative oxidants, with RuCl3‐NaIO4 in the presence of tetrabutylammonium bromide (TBAB) achieving the highest yield of 65 %. This optimized method was successfully applied to synthesize ibuprofen impurity E on a gram scale. Additionally, the applicability of these methods to obtain other API related substances, such as naproxen impurity L and ketoprofen impurity A, was demonstrated. This research highlights the potential of HTE to streamline the synthesis of API impurities, making them widely accessible for pharmaceutical development purposes.

Hyperconjugation-Driven Isodesmic Reaction of Indoles and Anilines: Reaction Discovery, Mechanism Study, and Antitumor Application.

Isodesmic reactions, in which chemical bonds are redistributed between substrates and products, provide a general and powerful strategy for both biological and chemical synthesis. However, most isodesmic reactions involve either metathesis or functional-group transfer. Here, we serendipitously discovered a novel isodesmic reaction of indoles and anilines that proceeds intramolecularly under weakly acidic conditions. In this process, the five-membered ring of the indole motif is broken and a new indole motif is constructed on the aniline side, accompanied by the formation of a new aniline motif. Mechanistic studies revealed the pivotal role of σ→π* hyperconjugation on the nitrogen atom of the indole motif in driving this unusual isodesmic reaction. Furthermore, we successfully synthesized a diverse series of polycyclic indole derivatives; among quinolines, potential antitumor agents were identified using cellular and in vivo experiments, thereby demonstrating the synthetic utility of the developed methodology.

Hyperconjugation‐Driven Isodesmic Reaction of Indoles and Anilines: Reaction Discovery, Mechanism Study, and Antitumor Application

AbstractIsodesmic reactions, in which chemical bonds are redistributed between substrates and products, provide a general and powerful strategy for both biological and chemical synthesis. However, most isodesmic reactions involve either metathesis or functional‐group transfer. Here, we serendipitously discovered a novel isodesmic reaction of indoles and anilines that proceeds intramolecularly under weakly acidic conditions. In this process, the five‐membered ring of the indole motif is broken and a new indole motif is constructed on the aniline side, accompanied by the formation of a new aniline motif. Mechanistic studies revealed the pivotal role of σ→π* hyperconjugation on the nitrogen atom of the indole motif in driving this unusual isodesmic reaction. Furthermore, we successfully synthesized a diverse series of polycyclic indole derivatives; among quinolines, potential antitumor agents were identified using cellular and in vivo experiments, thereby demonstrating the synthetic utility of the developed methodology.

Impact of C-H Cross-Coupling Reactions in the One-Step Retrosynthesis of Drug Molecules.

Pharmaceutical synthesis requires a diversity of chemical reactions. The discovery of new reactions enable novel retrosynthetic disconnections, potentially expediting access to complex molecules. This Synopsis demonstrates the use of enumerative combinatorics to find impactful underdeveloped reactions for drug synthesis. By mapping pharmaceutical target molecules onto available building blocks using just one retrosynthetic disconnection even if the requisite reaction is not yet known, we highlight the importance of site-selective C-H cross-coupling methods. This cheminformatics-driven retrosynthetic analysis identifies novel reaction methods of value to the synthesis toolbox.

Selective Functionalization of Alkenes and Alkynes by Dinuclear Manganese Catalysts.

ConspectusAlkenes and alkynes are fundamental building blocks in organic synthesis due to their commercial availability, bench-stability, and easy preparation. Selective functionalization of alkenes and alkynes is a crucial step for the synthesis of value-added compounds. Precise control over these reactions allows efficient construction of complex molecules with new functionalities. In recent decades, second- and third-row precious transition metal catalysts (palladium, platinum, rhodium, ruthenium) have been pivotal in the development of metal-catalyzed synthetic methodology. These metals exhibit excellent catalytic activity and selectivity, enabling efficient synthesis of functionalized organic molecules. However, recovery and reuse of precious metals have long been a challenge in this field. In recent years, exploration of earth-abundant metal-catalyzed organic reactions has interested both academic and industrial researchers. The development of such catalytic systems offers a promising approach to overcome the limitations of precious metal catalysts. For example, manganese is the third most naturally abundant transition metal with minimal toxicity and excellent biocompatibility. It exhibits good catalytic activity in several organic reactions, including C-H bond functionalization, selective reduction, and radical reactions. This Account outlines our recent progress in dinuclear manganese catalysis for selective functionalization of alkenes and alkynes. We have established the elementary manganese(I)-catalysis in transmetalation with R-B(OH)2. This finding has enabled us to apply the catalyst for the selective 1,2-difunctionalization of structurally diverse alkenes and alkynes. Mechanistic studies suggest a double manganese center synergistic activation model, as superior to Mn(CO)5Br in some cases. In addition, we have developed a ligand-tuned metalloradical strategy of dinuclear manganese catalysts (Mn2(CO)10), bridging the gap between the organometallics and radical chemistry, highlighting the unique radical functionalization of alkenes. Interestingly, using the same starting materials, different ligands can deliver completely different products. Meanwhile, a cooperative catalysis strategy involving manganese and other catalysts (e.g., cobalt, iminium) has also been developed and is briefly discussed. For manganese/iminium synergistic catalysis, a new mechanism for migratory insertion and demetalization-isomerization in synergistic HOMO-LUMO activation was disclosed. This strategy expands the application of low-valent manganese catalysts for enantioselective C-C bond-forming reactions. New reaction discovery is outpacing mechanism studies for dinuclear manganese catalysis, and future studies with time-resolved spectroscopy will improve understanding of the mechanism. Based on these intriguing findings, the precise functionalization of alkenes and alkynes by dinuclear manganese catalysts will expedite a novel activation model to enable late-stage functionalization of complex molecules.

Reinforcement Learning for Improving Chemical Reaction Performance.

Deep learning (DL) methods have gained notable prominence in predictive and generative tasks in molecular space. However, their application in chemical reactions remains grossly underutilized. Chemical reactions are intrinsically complex: typically involving multiple molecules besides bond-breaking/forming events. In reaction discovery, one aims to maximize yield and/or selectivity that depends on a number of factors, mostly centered on reacting partners and reaction conditions. Herein, we introduce RE-EXPLORE, a novel approach that integrates deep reinforcement learning (RL) with an RNN-based deep generative model to identify prospective new reactants/catalysts, whose yield/selectivity is estimated using a pretrained regressor. Three chemical databases (ChEMBL, ZINC, and COCONUT containing half a million to one million unlabeled molecules) are independently used for pretraining the generators to enrich them with valuable information from diverse chemical space. Standard RL methods are found to be insufficient, as learners tend to prioritize exploitation for immediate gains, resulting in repetitive generation of same/similar molecules. Our engineered reward function includes a Tanimoto-based uniqueness factor within the RL loop that improved the exploration of the environment and has helped accrue larger returns. Integration of a user-defined core fragment into the generated molecules facilitated learning of specific reaction types. Together, RE-EXPLORE can navigate the reaction space toward practically meaningful regions and offers notable improvements across the three distinct reaction types considered in this study. It identifies high-yielding substrates and highly enantioselective chiral catalysts. This RL-based approach has the potential to expedite reaction discovery and aid in the synthesis planning of important compounds, including drugs and pharmaceuticals.

Highlighting multicomponent reactions as an efficient and facile alternative route in the chemical synthesis of organic-based molecules: a tremendous growth in the past 5 years.

Since Strecker's discovery of multicomponent reactions (MCRs) in 1850, the strategy of applying an MCR approach has been in use for over a century. Due to their ability to quickly develop molecular diversity and structural complexity of interest, MCRs are considered an efficient approach in organic synthesis. Although MCRs such as the Ugi, Passerini, Biginelli, and Hantzsch reactions are widely studied, this review emphasizes the significance of selective MCRs to elegantly produce organic compounds of potential use in medicinal chemistry and industrial and material science applications, as well as the use of the MCR approach to sustainable methods. During synthesis, MCRs provide advantages such as atom economy, recyclable catalysts, moderate conditions, preventing waste, and avoiding solvent use. MCRs also reduce the number of sequential multiple reactions to one step.

Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset

AbstractMechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.

Reproducing Reaction Mechanisms with Machine-Learning Models Trained on a Large-Scale Mechanistic Dataset.

Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.

SARS-CoV-2 infections before, during, and after the Omicron wave: a 2-year Indian community cohort study

We measured the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and re-infections in an adult community-based cohort in southern India. We conducted a 2-year follow-up on 1229 participants enrolled between May and October 2021. Participants provided vaccination histories, weekly saliva samples, and blood samples at 0, 6, 12, and 24 months. Salivary reverse transcription polymerase chain reaction (RT-PCR) and Meso-Scale Discovery panels were used for SARS-CoV-2 detection and anti-spike, anti-nucleocapsid immunoglobulin G quantification. Whole genome sequencing was performed on a subset of positive samples. SARS-CoV-2 infection incidence was measured across Pre-Omicron (May-December 2021), Omicron-I (December 2021-June 2022), and Omicron-II (July 2022-October 2023) periods. In total, 1166 (95%) participants with 83% seropositivity at baseline completed the follow-up, providing 2205 person-years of observation. Utilizing both RT-PCR and serology we identified 1306 infections and yielded an incidence rate of 591.3 per 1000 person-years (95% confidence interval, 559.6-624.3), which peaked during Omicron-I at 1418.1 per 1000 person-years (95% confidence interval, 1307.4-1535.6). During Omicron-I and II, neither prior infection nor vaccination conferred protection against infection. Overall, 74% of infections were asymptomatic. Integrated RT-PCR and serology revealed significant SARS-CoV-2 infection frequency, highlighting the prevalence of asymptomatic cases among previously infected or vaccinated individuals. This underscores the effectiveness of combining surveillance strategies when monitoring pandemic trends and confirms the role of non-invasive sampling in ensuring participant compliance, reflecting national transmission patterns. The study was funded by the Bill and Melinda Gates Foundation.

Parallel and High Throughput Reaction Monitoring with Computer Vision.

We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well-plate high throughput settings, are described. Analysis of parallel dye-quenching hydroxylations, DMAP-catalysed esterification, solid-liquid sedimentation dynamics, metal catalyst degradation, and biologically-relevant sugar-mediated nitro reduction reactions have each provided insight into the scope and limitations of camera-enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi-reaction time-resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

Reactant Discovery with an Ab Initio Nanoreactor: Exploration of Astrophysical N-Heterocycle Precursors and Formation Pathways.

The incorporation of nitrogen atoms into cyclic compounds is essential for terrestrial life; nitrogen-containing (N-)heterocycles make up DNA and RNA nucleobases, several amino acids, B vitamins, porphyrins, and other components of biomolecules. The discovery of these molecules on meteorites with non-terrestrial isotopic abundances supports the hypothesis of exogenous delivery of prebiotic material to early Earth; however, there has been no detection of these species in interstellar environments, indicating that there is a need for greater knowledge of their astrochemical formation and destruction pathways. Here, we present results of simulations of gas-phase pyrrole and pyridine formation from an ab initio nanoreactor, a first-principles molecular dynamics simulation method that accelerates reaction discovery by applying non-equilibrium forces that are agnostic to individual reaction coordinates. Using the nanoreactor in a retrosynthetic mode, starting with the N-heterocycle of interest and a radical leaving group, then considering the discovered reaction pathways in reverse, a rich landscape of N-heterocycle-forming reactivity can be found. Several of these reaction pathways, when mapped to their corresponding minimum energy paths, correspond to novel barrierless formation pathways for pyridine and pyrrole, starting from both detected and hypothesized astrochemical precursors. This study demonstrates how first-principles reaction discovery can build mechanistic knowledge in astrochemical environments as well as in early Earth models such as Titan's atmosphere where N-heterocycles have been tentatively detected.

Kinetic Resolution as a General Approach to Enantioenrichment in Prebiotic Chemistry.

ConspectusThe origin of the single chirality of the chemical building blocks of life remains an intriguing topic of research, even after decades of experimental and theoretical work proposing processes that may break symmetry and induce chiral amplification, a term that may be defined as the enhancement of enantiomeric excess starting from prochiral substrates or from a racemic mixture or a small imbalance between enantiomers. Studies aimed at understanding prebiotically plausible pathways to these molecules have often neglected the issue of chirality, with a focus on the stereochemical direction of these reactions generally being pursued after reaction discovery. Our work has explored how the stereochemical outcome for the synthesis of amino acids and sugars might be guided to rationalize the origin of biological homochirality. The mechanistic interconnection between enantioenrichment in these two groups of molecules provides insights concerning the handedness extant in modern biology. In five separate examples involving the synthesis of life's building blocks, including sugars, RNA precursors, amino acids, and peptides, kinetic resolution emerges as a key protocol for enantioenrichment from racemic molecules directed by chiral source molecules. Several of these examples involve means not only for chiral amplification but also symmetry breaking and chirality transfer across a range of racemic monomer molecules. Several important implications emerge from these studies: one, kinetic resolution of the primordial chiral sugar, glyceraldehyde, plays a key role in a number of different prebiotically plausible reactions; two, the emergence of homochirality in sugars and amino acids is inherently intertwined, with clear synergy between the biological hand of each molecule class; three, the origin story for the homochirality of enzymes and modern metabolism points toward kinetic resolution of racemic amino acids in networks that later evolved to include sophisticated and complete catalytic and co-catalytic cycles; four, a preference for heterochiral ligation forming product molecules that cannot lead to biologically competent polymers can in fact be a driving force for a route to homochiral polymer chains; and five, enantioenrichment in complex mixtures need not be addressed one compound at a time, because kinetic resolution induces symmetry breaking and chirality transfer that may lead to general protocols rather than specific cases tailored to each individual molecule. Such chirality transfer mechanisms perhaps presage strategies utilized in modern biology.Our latest work extends the study of monomer enantioenrichment to the ligation of these molecules into the extended homochiral chains leading to the complex polymers of modern biology. A central theme in all of these reactions is the key role that kinetic resolution of a racemic mixture of amino acids or sugars plays in enabling enantioenrichment under prebiotically plausible conditions. This work has uncovered important trends in symmetry breaking, chirality transfer, and chiral amplification. Kinetic resolution of racemic mixtures emerges as a general solution for chiral amplification in prebiotic chemistry, leading to the single chirality of complex biological molecules and genetic polymers.

Reaction Discovery Using Spectroscopic Insights from an Enzymatic C-H Amination Intermediate.

Engineered hemoproteins can selectively incorporate nitrogen from nitrene precursors like hydroxylamine, O-substituted hydroxylamines, and organic azides into organic molecules. Although iron-nitrenoids are often invoked as the reactive intermediates in these reactions, their innate reactivity and transient nature have made their characterization challenging. Here we characterize an iron-nitrosyl intermediate generated from NH2OH within a protoglobin active site that can undergo nitrogen-group transfer catalysis, using UV-vis, electron paramagnetic resonance (EPR) spectroscopy, and high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) techniques. The mechanistic insights gained led to the discovery of aminating reagents─nitrite (NO2-), nitric oxide (NO), and nitroxyl (HNO)─that are new to both nature and synthetic chemistry. Based on the findings, we propose a catalytic cycle for C-H amination inspired by the nitrite reductase pathway. This study highlights the potential of engineered hemoproteins to access natural nitrogen sources for sustainable chemical synthesis and offers a new perspective on the use of biological nitrogen cycle intermediates in biocatalysis.

Deep Kernel learning for reaction outcome prediction and optimization

Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the feature learning ability of neural networks (NNs) with uncertainty quantification of GPs in a deep kernel learning (DKL) framework to predict the reaction outcome. The DKL model is observed to obtain very good predictive performance across different input representations. It significantly outperforms standard GPs and provides comparable performance to graph neural networks, but with uncertainty estimation. Additionally, the uncertainty estimates on predictions provided by the DKL model facilitated its incorporation as a surrogate model for Bayesian optimization (BO). The proposed method, therefore, has a great potential towards accelerating reaction discovery by integrating accurate predictive models that provide reliable uncertainty estimates with BO.

Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides, a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigma-profile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.