Prediction Of Regioselectivity Research Articles

Recent Advances in Transition Metal-Catalyzed Ring-Opening Reaction of Aziridine

The smallest strained, saturated N-heterocycles, such as aziridine, can be a valuable building block in synthetic organic chemistry. Ring-opening reactions with various nucleophiles could be the most important strategy to synthesize various value-added molecular entities. Therefore, regioselective ring-opening reactions of aziridines with various heteroatomic nucleophiles and carbon nucleophiles establish a useful synthetic methodology to synthesize biologically relevant β-functionalized alkylamines. The regio-selective ring-opening of aziridines is highly dependent on the substrate combination, and stereochemical control is challenging for Lewis acid-promoted reactions. Therefore, the development of a robust, catalytic ring-opening process that assists in the accurate prediction of regioselectivity and stereochemistry is highly desirable. Consequently, a large number of publications detailing distinct methods for aziridine ring-opening reactions can be found in the literature. In this review, we discuss several transition metal catalyzed cross-coupling reaction protocols for the ring opening of substituted aziridines with various carbon nucleophiles.

Personalized machine learning models of terminal olefin hydroformylation for regioselectivity prediction

Personalized machine learning models of terminal olefin hydroformylation for regioselectivity prediction

Hydrogen Source Tuned Regiodivergent Asymmetric Hydroalkylations of 2‐Substituted 1,3‐Dienes with Aldehydes by Cobalt‐Catalysis

AbstractCatalytic methods allowing for the reliable prediction and control of diverse regioselectivity along with the control of enantioselectivity to access different regio‐ and enantiomers by switching the least reaction parameters are one of the most attractive ways in organic synthesis, which provide access to diverse enantioenriched architectures from identical starting materials. Herein, a Co‐catalyzed regiodivergent and enantioselective reductive hydroalkylation of 1,3‐dienes with aldehydes has been achieved, furnishing different enantioenriched homoallylic alcohol architectures in good levels of enantioselectivity. The reaction features the switch of regioselectivity tuned by the selection of proton source. The use of an acid as proton source provided asymmetric 1,2‐hydroalkylation products under reductive conditions, yet asymmetric 4,3‐hydroalkylation products were obtained with silane as hydride source. This catalytic protocol allows for the access of homoallylic alcohols with two continuous saturated carbon centers in good levels of regio‐, diastereo‐, and enantioselectivity.

Hydrogen Source Tuned Regiodivergent Asymmetric Hydroalkylations of 2-Substituted 1,3-Dienes with Aldehydes by Cobalt-Catalysis.

Catalytic methods allowing for the reliable prediction and control of diverse regioselectivity along with the control of enantioselectivity to access different regio- and enantiomers by switching the least reaction parameters are one of the most attractive ways in organic synthesis, which provide access to diverse enantioenriched architectures from identical starting materials. Herein, a Co-catalyzed regiodivergent and enantioselective reductive hydroalkylation of 1,3-dienes with aldehydes has been achieved, furnishing different enantioenriched homoallylic alcohol architectures in good levels of enantioselectivity. The reaction features the switch of regioselectivity tuned by the selection of proton source. The use of an acid as proton source provided asymmetric 1,2-hydroalkylation products under reductive conditions, yet asymmetric 4,3-hydroalkylation products were obtained with silane as hydride source. This catalytic protocol allows for the access of homoallylic alcohols with two continuous saturated carbon centers in good levels of regio-, diastereo-, and enantioselectivity.

SNAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold.

Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for SNAr reactions. Our method is accurate and fast, which achieves 96.3 and 94.7% accuracy in predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions.

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability.

There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generalizability, and explainability of the quantum mechanics-augmented graph neural network (ml-QM-GNN) architecture as applied to the prediction of regioselectivity (classification) and of activation energies (regression). In our hybrid QM-augmented model architecture, structure-based representations are first used to predict a set of atom- and bond-level reactivity descriptors derived from density functional theory calculations. These estimated reactivity descriptors are combined with the original structure-based representation to make the final reactivity prediction. We demonstrate that our model architecture leads to significant improvements over structure-based GNNs in not only overall accuracy but also in generalization to unseen compounds. Even when provided training sets of only a couple hundred labeled data points, the ml-QM-GNN outperforms other state-of-the-art structure-based architectures that have been applied to these tasks as well as descriptor-based (linear) regressions. As a primary contribution of this work, we demonstrate a bridge between data-driven predictions and conceptual frameworks commonly used to gain qualitative insights into reactivity phenomena, taking advantage of the fact that our models are grounded in (but not restricted to) QM descriptors. This effort results in a productive synergy between theory and data science, wherein QM-augmented models provide a data-driven confirmation of previous qualitative analyses, and these analyses in turn facilitate insights into the decision-making process occurring within ml-QM-GNNs.

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C–H functionalization, aromatic C–X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings.

Enzyme Kinetics, Pharmacokinetics, and Inhibition of Aldehyde Oxidase.

Aldehyde oxidase (AO) has emerged as an important drug metabolizing enzyme over the last decade. Several compounds have failed in the clinic because the clearance or toxicity was underestimated by preclinical species. Human AO is much more active than rodent AO, and dogs do not have functional AO. Metabolic products from AO-catalyzed oxidation are generally nonreactive and often they have much lower solubility. AO metabolism is not limited to oxidation as AO can also catalyze reduction of oxygen and nitrite. Reduction of oxygen leads to the reactive oxygen species (ROS) superoxide radical anion and hydrogen peroxide. Reduction of nitrite leads to the formation of nitric oxide with potential pharmacological implications. AO is also reported to catalyze the reductive metabolism of nitro-compounds, N-oxides, sulfoxides, isoxazoles, isothiazoles, nitrite, and hydroxamic acids. These reductive transformations may cause toxicity due to the formation of reactive metabolites. Moreover, the inhibition kinetics are complex, and multiple probe substrates should be used when assessing the potential for DDIs. Finally, AO appears to be amenable to computational predictions of both regioselectivity and rates of reaction, which holds promise for virtual screening.

Palladium-Catalyzed Regioselective and Stereospecific Ring-Opening Cross-Coupling of Aziridines: Experimental and Computational Studies.

Aziridines, i.e., the smallest saturated N-heterocycles, serve as useful building blocks in synthetic organic chemistry. Because of the release of the large ring strain energy accommodated in the small ring, (ca. 27 kcal/mol), aziridines undergo ring-opening reactions with a variety of nucleophiles. Therefore, among the synthetic reactions utilizing aziridines, regioselective ring-opening substitutions of aziridines with nucleophiles, such as heteroatomic nucleophiles (e.g., amines, alcohols, and thiols) and carbonaceous nucleophiles (e.g., carbanions, organometallic reagents, and electron-rich arenes), constitute a useful synthetic methodology to synthesize biologically relevant β-functionalized alkylamines. However, the regioselection in such traditional ring-opening substitutions of aziridines is highly dependent on the substrate combination, and stereochemical control is challenging to achieve, especially in the case of Lewis acid-promoted variants. Therefore, the development of robust catalytic ring-opening functionalization methods that enable precise prediction of regioselectivity and stereochemistry is desirable. In this direction, our group focused on the highly regioselective and stereospecific nature of the stoichiometric oxidative addition elementary step of 2-substituted aziridines into Pd(0) complexes in an SN2 fashion. In conjunction with the recent advancements in transition-metal-catalyzed cross-coupling reactions of alkyl pseudohalides containing a C(sp3)-Q (Q = O, N, S, etc.) bond, aziridines can be used as nonclassical alkyl pseudohalides in regioselective and stereospecific cross-couplings.In this Account, starting from the background of transition-metal-catalyzed ring-opening functionalization of aziridines, our contributions to the palladium-catalyzed regioselective and stereoinvertive cross-couplings of aziridines with organoboron reagents to form C(sp3)-C, C(sp3)-B, and C(sp3)-Si bonds have been compiled. The developed methods allow the syntheses of medicinally important amine compounds, e.g., enantioenriched β-phenethylamines, β-amino acids, and their boron and silyl surrogates, from readily available enantiopure aziridine substrates. Notably, the regioselectivity of the ring opening can be switched by appropriate selection of the catalyst (i.e., Pd/NHC vs Pd/PR3 systems). Computational studies rationalized the detailed mechanisms of the full catalytic cycle and the regioselectivity and stereospecificity of the reactions. The computational results suggested that the interactions operating between the Pd catalyst and aziridine substrate play important roles in determining the regioselection of the aziridine ring-opening event (i.e., oxidative addition). Also, the computational results rationalized the role of water molecules in promoting the transmetalation step through the formation of a Pd-hydroxide active intermediate. This Account evidences the benefits of synergistic collaborations between experimental and computational methods in developing novel transition-metal-catalyzed cross-coupling reactions.

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

AbstractRadical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out‐of‐sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism‐based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.

Predicting Regioselectivity in Radical C-H Functionalization of Heterocycles through Machine Learning.

Radical C-H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C-H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.

Diastereoselective desymmetric 1,2-cis-glycosylation of meso-diols via chirality transfer from a glycosyl donor

Chemical desymmetrization reactions of meso-diols are highly effective for the precise and efficient synthesis of chiral molecules. However, even though enzyme-catalyzed desymmetric glycosylations are frequently found in nature, there is no method for highly diastereoselective desymmetric chemical glycosylation of meso-diols. Herein, we report a highly diastereoselective desymmetric 1,2-cis-glycosylation of meso-diols found in myo-inositol 1,3,5-orthoesters using a boronic acid catalyst based on predictions of regioselectivity by density functional theory (DFT) calculations. The enantiotopic hydroxyl groups of the meso-diols are clearly differentiated by the stereochemistry at the C2 position of the glycosyl donor with excellent regioselectivities. In addition, the present method is successfully applied to the synthesis of core structures of phosphatidylinositolmannosides (PIMs) and glycosylphosphatidylinositol (GPI) anchors, and common β-mannoside structures of the LLBM-782 series of antibiotics.

Prediction of regioselectivity and preferred order of CYP1A1-mediated metabolism: Solving the interaction of human and rat CYP1A1 forms with ligands on the template system

A simulation system for ligand interaction of human CYP1A1 has been developed using “Template” composed of hexagonal grids, as a modification of CYP1A2 system established previously. Differing from CYP1A2 Template, Site of Oxidation of CYP1A1 was located one-grid (Ring) away horizontally from Trigger-Region (Ring B) on CYP1A1 Template. Simultaneous interaction at Site of Oxidation and Trigger-Region as uni- or bi-molecule binding was maintained with CYP1A1 as well as CYP1A2 for the functional contributions. Reciprocal comparison of simulation results with experimental data suggested the access of ligands to Site of Oxidation inside of CYP1A1, through three distinct routes, termed Sideway, Center-Area and Thick-Area. To facilitate the verification of feasible placement(s), typical modes of the regional interactions have been defined and developed for prognostic devices. Simulation experiments of human and rat CYP1A1 offered possible causative mechanisms of the species difference as their distinct interactions near Site of Oxidation. The present CYP1A1 Template system has been proven to afford regio- and stereo-chemically feasible placements, through the use of the prognostic devices, of total of 353 CYP1A1-mediated reactions of 223 of distinct ligands, including substrates, inhibitors and poor substrates of drugs, environmental chemicals and endobiotics.

Relative Strength of Common Directing Groups in Palladium-Catalyzed Aromatic C-H Activation.

SummaryEfficient functionalization of C−H bonds can be achieved using transition metal catalysts, such as Pd(OAc)2. To better control the regioselectivity in these reactions, some functional groups on the substrate may be used as directing groups, guiding the reactivity to an ortho position. Herein, we describe a methodology to score the relative strength of such directing groups in palladium-catalyzed aromatic C−H activation. The results have been collected into a scale that serves to predict the regioselectivity on molecules with multiple competing directing groups. We demonstrate that this scale yields accurate predictions on over a hundred examples, taken from the literature. In addition to the regioselectivity prediction on complex molecules, the knowledge of the relative strengths of directing groups can also be used to work with new combinations of functionalities, exploring uncharted chemical space.

Prediction of regioselectivity and preferred order of metabolisms on CYP1A2-mediated reactions part 3: Difference in substrate specificity of human and rodent CYP1A2 and the refinement of predicting system

Differences in CYP1A2-mediated metabolisms in human, rat and mouse have been analyzed with Template of human CYP1A2 established in our previous studies (Drug Metab Pharmacokinet 31:363, 2016 and 32:229, 2017). Using more than 25 chemicals including phenanthrene, MeIQx, PhIP, caffeine and furafylline, Template for human CYP1A2 was found to be applicable for rat and mouse CYP1A2 reactions with the consideration of five distinct regional interactions: 1) Expanded use of Ring D region of pro-metabolized molecules and also of trigger molecules, 2) acceptance of secondary amino groups at Position 31 of Ring eC1, 3) overlapping of pro-metabolized and trigger molecules at Ring eC4, 4) restricted maneuvering of substrates into Bay 1 region, and 5) allowance of passage of slightly large ligands in Thin-Area. These distinction points were found to be mutual for both substrates and inhibitors. In the present study, the decision-tree for substrates entering from Thin-Area has been reevaluated in consideration of species differences in human and rodent CYP1A2 forms. As the results, five steps of verification procedures have been introduced to predict the occurrence order of the regioselective metabolisms.

Solving substrate interactions of CYP1A2 with non-PAH substrates on the template system: Prediction of regioselectivity and preferred order of metabolisms on CYP1A2-mediated reactions

Solving substrate interactions of CYP1A2 with non-PAH substrates on the template system: Prediction of regioselectivity and preferred order of metabolisms on CYP1A2-mediated reactions

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity

2013, 53, 2896-2907), a collection of random forest models for the prediction of sites of metabolism (SoMs) of xenobiotics. A broad set of descriptors was explored, from simple 2D descriptors such as those used in FAME, to quantum chemical descriptors employed in some of the most accurate models for SoM prediction currently available. In line with the original FAME approach, our objective was to keep things simple and to come up with accurate and robust models that are based on a small number of 2D descriptors. We found that circular descriptions of atoms and their environments with such descriptors in combination with an extremely randomized trees algorithm can yield models that perform equally well compared to more complex approaches. Thorough evaluation experiments on an independent test set showed that the best of these models obtained a Matthews correlation coefficient, area under the receiver operating characteristic curve, and Top-2 accuracy of 0.57, 0.91 and 94.1%, respectively. Models for the prediction of isoform-specific regioselectivity of CYP 3A4, 2D6, and 2C9 were also developed and showed competitive performance. The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors.

Prediction of regioselectivity and preferred order of metabolisms on CYP1A2-mediated reactions. Part 2: Solving substrate interactions of CYP1A2 with non-PAH substrates on the template system

In our previous paper (Drug Metabolism Parmacokinet31: 363, 2016), a simulation system for ligand interactions of human CYP1A2 was developed using “Template” composed of hexagonal grids focusing on polyaromatic hydrocarbons (PAHs). The system has been expanded for the application of non-PAH chemicals including medical drugs, industrial chemicals and natural products in the present study. Additions of two Templates C and D around Ring C/E and Ring B, respectively, perpendicular each to Template A, offered the accommodation of non-PAH substrates. The size and shape of Templates C and D were defined from the reciprocal comparison of experimental data of ligands with simulation on Templates. The requirements of occupancies at Trigger region (Ring B) and at region of Facial-side Movement (Ring C) as well as Site of Oxidation were found to be mutual throughout CYP1A2 good substrates tested for over the 450 reactions, irrespective of their shapes and flexibilities. The CYP1A2 Template system was also verified with distinct types of poor substrates (47 chemicals) and inhibitors (37 inhibitors) and found to offer the information on probable structural causes. Present CYP1A2 Template system offers a unified evaluation of human CYP1A2 ligands, which aids for toxicological assessments as well as drug metabolism studies.

Mechanistic Insights into Scandium-Catalyzed Hydroaminoalkylation of Olefins with Amines: Origin of Regioselectivity and Charge-Based Prediction Model

DFT calculations have been carried out on the Sc-catalyzed regioselective hydroaminoalkylation of olefins with amines. It has been revealed that the true active species is an amine-coordinated η2-azametallacyclic complex, and electronic factors play a crucial role in achieving regioselectivity. The charge dispersion and charge alternation account well for the stability of the olefin insertion transition states and products. The charge distribution of 15 olefin substrates used previously in the experimental studies correlates well with the observed regioselectivity and could thus provide a potential model for regioselectivity prediction. The reactivity of different types of C–H bonds was also explored by modeling the reaction of (CH2C6H4NMe2-o)2Sc+ with iPrN(Me)(Et). The suggested reactivity trend for the Sc-catalyzed hydroaminoalkylation follows the order of primary C–H bond > secondary C–H bond > tertiary C–H bond.