Accurate heteronuclear distance measurements at all magic-angle spinning frequencies in solid-state NMR spectroscopy

Heteronuclear dipolar coupling is indispensable in revealing vital information related to the molecular structure and dynamics, as well as intermolecular interactions in various solid materials. Although numerous approaches have been developed to selectively reintroduce heteronuclear dipolar coupling under MAS, most of them lack universality and can only be applied to limited spin systems. Herein, we introduce a new and robust technique dubbed phase modulated rotary resonance (PMRR) for reintroducing heteronuclear dipolar couplings while suppressing all other interactions under a broad range of MAS conditions. The standard PMRR requires the radiofrequency (RF) field strength of only twice the MAS frequency, can efficiently recouple the dipolar couplings with a large scaling factor of 0.50, and is robust to experimental imperfections. Moreover, the adjustable window modification of PMRR, dubbed wPMRR, can improve its performance remarkably, making it well suited for the accurate determination of dipolar couplings in various spin systems. The robust performance of such pulse sequences has been verified theoretically and experimentally via model compounds, at different MAS frequencies. The application of the PMRR technique was demonstrated on the H-ZSM-5 zeolite, where the interaction between the Brønsted acidic hydroxyl groups of H-ZSM-5 and the absorbed trimethylphosphine oxide (TMPO) were probed, revealing the detailed configuration of super acid sites.

A new and robust technique dubbed phase modulated rotary resonance (PMRR) was proposed for the accurate determination of heteronuclear dipolar coupling under a broad range of MAS conditions in solid-state NMR spectroscopy.     

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.     

Coumarin luciferins and mutant luciferases for robust multi-component bioluminescence imaging

Multi-component bioluminescence imaging requires an expanded collection of luciferase–luciferin pairs that emit far-red or near-infrared light. Toward this end, we prepared a new class of luciferins based on a red-shifted coumarin scaffold. These probes (CouLuc-1s) were accessed in a two-step sequence via direct modification of commercial dyes. The bioluminescent properties of the CouLuc-1 analogs were also characterized, and complementary luciferase enzymes were identified using a two-pronged screening strategy. The optimized enzyme-substrate pairs displayed robust photon outputs and emitted a significant portion of near-infrared light. The CouLuc-1 scaffolds are also structurally distinct from existing probes, enabling rapid multi-component imaging. Collectively, this work provides novel bioluminescent tools along with a blueprint for crafting additional fluorophore-derived probes for multiplexed imaging.

Near-infrared probes were developed from coumarin-modified luciferins and engineered luciferases, enabling facile multiplexed bioluminescence imaging.     

Chemo- and regio-divergent access to fluorinated 1-alkyl and 1-acyl triazenes from alkynyl triazenes

The 1,1,2,2-tetrafluoroethylene unit is prevalent in bioactive molecules and functional materials. Despite being in principle a straightforward strategy to access this motif, the direct tetrafluorination of alkynes involves very hazardous or inconvenient reagents. Therefore, safer and convenient alternatives are sought after. We developed a mild and operationally simple perfluorination method converting 1-alkynyl triazenes into 1,1,2,2-tetrafluoro alkyl triazenes, employing cheap and readily accessible reagents. Moreover, a judicious tuning of the reaction conditions enables access to α-difluoro triazenyl ketones. Complementary, electrophilic fluorination of alkynyl triazenes gives rise to the regioisomeric α-difluoro acyl triazenes. These three chemo- and regio-divergent protocols enable access to elusive fluorinated 1-alkyl and 1-acyl triazenes, thus expanding the chemical space for these unusual entities. Furthermore, several reaction intermediates and side products revealed insights on the reaction pathways that may be useful for further fluorination chemistry of alkynes.

Three mild and operationally simple fluorination protocols convert 1-alkynyl triazenes either into attractive 1,1,2,2-tetrafluoro alkyl triazenes, α-difluoro α-triazenyl ketones or α-difluoro acyl triazenes.     

The dioxasilepanyl group as a versatile organometallic unit: studies on stability,reactivity, and utility

Organic synthesis is performed based on precise choices of functional groups and reactions employed. In a multistep synthesis, an ideal functional group should be compatible with various reaction conditions and unaltered until it is subjected to a selective conversion. The current study was set out to search for a silicon functionality that meets these criteria. Here we have established a new silicon-based synthetic methodology centred on a bulky 7-membered dialkoxysilyl group (2,4,4,7,7-pentamethyl-1,3,2-dioxasilepan-2-yl) that uniquely has both stability and on-demand reactivity. The exceptional stability of this functional group was corroborated by both experimental and computational studies which demonstrated that key factors for its stability were a 7-membered structure and steric hindrance. In turn, the dioxasilepanyl group was found to become reactive and to be easily transformed in the presence of appropriate activators. Combined with the development of easy and robust methods to introduce the dioxasilepanyl group onto aryl rings, these findings have allowed a shorter and more efficient synthesis of a bioactive molecule, thus demonstrating the potential utility of the easily accessible dioxasilepanyl group in organic synthesis.

A bulky 7-membered dioxasilepanyl group has been established as a new organometallic unit for multistep organic syntheses.     

Silicon – single molecule – silicon circuits

In 2020, silicon – molecule – silicon junctions were fabricated and shown to be on average one third as conductive as traditional junctions made using gold electrodes, but in some instances to be even more conductive, and significantly 3 times more extendable and 5 times more mechanically stable. Herein, calculations are performed of single-molecule junction structure and conductivity pertaining to blinking and scanning-tunnelling-microscopy (STM) break junction (STMBJ) experiments performed using chemisorbed 1,6-hexanedithiol linkers. Some strikingly different characteristics are found compared to analogous junctions formed using the metals which, to date, have dominated the field of molecular electronics. In the STMBJ experiment, following retraction of the STM tip after collision with the substrate, unterminated silicon surface dangling bonds are predicted to remain after reaction of the fresh tips with the dithiol solute. These dangling bonds occupy the silicon band gap and are predicted to facilitate extraordinary single-molecule conductivity. Enhanced junction extendibility is attributed to junction flexibility and the translation of adsorbed molecules between silicon dangling bonds. The calculations investigate a range of junction atomic-structural models using density-functional-theory (DFT) calculations of structure, often explored at 300 K using molecular dynamics (MD) simulations. These are aided by DFT calculations of barriers for passivation reactions of the dangling bonds. Thermally averaged conductivities are then evaluated using non-equilibrium Green''s function (NEGF) methods. Countless applications through electronics, nanotechnology, photonics, and sensing are envisaged for this technology.

Single-molecule circuits using silicon contacts are robust, conductive, controllable, and highly reproducible in blinking experiments, with enhanced conductance in break-junctions owing to residual dangling bonds.     

Nickel-catalyzed enantioselective 1,2-vinylboration of styrenes

A novel nickel-catalyzed asymmetric 1,2-vinylboration reaction has been developed to afford benzylic alkenylboration products with high yields and excellent enantioselectivities by using a chiral bisoxazoline ligand. Under optimized conditions, a wide variety of chiral 2-boryl-1,1-arylvinylalkanes are efficiently prepared from readily available olefins and vinyl halides in the presence of bis(pinacolato)diboron as the boron source in a mild and easy-to-operate manner. This three-component cascade protocol furnishes exceptional chemo- and stereoselectivity, and its usefulness is illustrated by its application in asymmetric modifications of several structurally complex natural products and pharmaceuticals.

A novel nickel-catalyzed asymmetric 1,2-vinylboration reaction has been developed to afford benzylic alkenylboration products with high yields and excellent enantioselectivities by using a chiral bisoxazoline ligand.     

DeepReac+: deep active learning for quantitative modeling of organic chemical reactions

Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.

Based on GNNs and active learning, DeepReac+ is designed as a universal framework for quantitative modeling of chemical reactions. It takes molecular structures as inputs directly and adapts to various prediction tasks with fewer training data.     

Peptoid-based reprogrammable template for cell-permeable inhibitors of protein–protein interactions

The development of inhibitors of intracellular protein–protein interactions (PPIs) is of great significance for drug discovery, but the generation of a cell-permeable molecule with high affinity to protein is challenging. Oligo(N-substituted glycines) (oligo-NSGs), referred to as peptoids, are attractive as potential intracellular PPI inhibitors owing to their high membrane permeability. However, their intrinsically flexible backbones make the rational design of inhibitors difficult. Here, we propose a peptoid-based rational approach to develop cell-permeable PPI inhibitors using oligo(N-substituted alanines) (oligo-NSAs). The rigid structures of oligo-NSAs enable independent optimization of each N-substituent to improve binding affinity and membrane permeability, while preserving the backbone shape. A molecule with optimized N-substituents inhibited a target PPI in cells, which demonstrated the utility of oligo-NSA as a reprogrammable template to develop intracellular PPI inhibitors.

A peptoid-based modular approach using oligo(N-substituted alanine) as a reprogrammable template enables independent optimization of N-substituents and facile development of cell-permeable inhibitors of protein–protein interactions.     

Can super-resolution microscopy become a standard characterization technique for materials chemistry?

The characterization of newly synthesized materials is a cornerstone of all chemistry and nanotechnology laboratories. For this purpose, a wide array of analytical techniques have been standardized and are used routinely by laboratories across the globe. With these methods we can understand the structure, dynamics and function of novel molecular architectures and their relations with the desired performance, guiding the development of the next generation of materials. Moreover, one of the challenges in materials chemistry is the lack of reproducibility due to improper publishing of the sample preparation protocol. In this context, the recent adoption of the reporting standard MIRIBEL (Minimum Information Reporting in Bio–Nano Experimental Literature) for material characterization and details of experimental protocols aims to provide complete, reproducible and reliable sample preparation for the scientific community. Thus, MIRIBEL should be immediately adopted in publications by scientific journals to overcome this challenge. Besides current standard spectroscopy and microscopy techniques, there is a constant development of novel technologies that aim to help chemists unveil the structure of complex materials. Among them super-resolution microscopy (SRM), an optical technique that bypasses the diffraction limit of light, has facilitated the study of synthetic materials with multicolor ability and minimal invasiveness at nanometric resolution. Although still in its infancy, the potential of SRM to unveil the structure, dynamics and function of complex synthetic architectures has been highlighted in pioneering reports during the last few years. Currently, SRM is a sophisticated technique with many challenges in sample preparation, data analysis, environmental control and automation, and moreover the instrumentation is still expensive. Therefore, SRM is currently limited to expert users and is not implemented in characterization routines. This perspective discusses the potential of SRM to transition from a niche technique to a standard routine method for material characterization. We propose a roadmap for the necessary developments required for this purpose based on a collaborative effort from scientists and engineers across disciplines.

SRM, an advanced nanoscopy technique demands a transition from being a niche sophisticated technique to standard routine method for material characterization. The roadmap of necessary developments through multidisciplinary collaboration is discussed.     

Mixed lead–tin perovskite films with >7 μs charge carrier lifetimes realized by maltol post-treatment

Mixed lead–tin (Pb–Sn) halide perovskites with optimum band gaps near 1.3 eV are promising candidates for next-generation solar cells. However, the performance of solar cells fabricated with Pb–Sn perovskites is restricted by the facile oxidation of Sn(ii) to Sn(iv), which induces self-doping. Maltol, a naturally occurring flavor enhancer and strong metal binding agent, was found to effectively suppress Sn(iv) formation and passivate defects in mixed Pb–Sn perovskite films. When used in combination with Sn(iv) scavenging, the maltol surface treatment led to high-quality perovskite films which showed enhanced photoluminescence intensities and charge carrier lifetimes in excess of 7 μs. The scavenging and surface treatments resulted in highly reproducible solar cell devices, with photoconversion efficiencies of up to 21.4% under AM1.5G illumination.

Maltol, a metal binding agent, effectively passivates defects on the surface of mixed lead–tin perovskite films. The carrier lifetimes of the resultant perovskite films are over 7 μs. The solar cell devices exhibit efficiencies of up to 21.4%.     

Atroposelective synthesis of N-aryl peptoid atropisomers via a palladium(ii)-catalyzed asymmetric C–H alkynylation strategy

The introduction of chirality into peptoids is an important strategy to determine a discrete and robust secondary structure. However, the lack of an efficient strategy for the synthesis of structurally diverse chiral peptoids has hampered the studies. Herein, we report the efficient synthesis of a wide variety of N-aryl peptoid atropisomers in good yields with excellent enantioselectivities (up to 99% yield and 99% ee) by palladium-catalyzed asymmetric C–H alkynylation. The inexpensive and commercially available l-pyroglutamic acid was used as an efficient chiral ligand. The exceptional compatibility of the C–H alkynylation with various peptoid oligomers renders this procedure valuable for peptoid modifications. Computational studies suggested that the amino acid ligand distortion controls the enantioselectivity in the Pd/l-pGlu-catalyzed C–H bond activation step.

The introduction of chirality into peptoids is an important strategy to determine a discrete and robust secondary structure.     

Linker depletion for missing cluster defects in non-UiO metal–organic frameworks

Defect engineering is a valuable tool to tune the properties of metal–organic frameworks. However, defect chemistry remains still predominantly limited to UiO-type MOFs. We describe the preferential formation of missing cluster defects in heterometallic titanium–organic frameworks of the MUV-10 family when synthesised in sub-stoichiometric linker conditions. Our results show the value of integrating experimental work, computational modelling and thorough characterization in rationalizing the impact of defects over the porosity and structure of this family of materials. Correlation of experiment with computational models reveals the dominance of missing cluster vacancies in the pore size distribution of defective MUV-10. These same models were used to investigate the correlation of defects by synchrotron X-ray diffraction. The diffraction at low reflection angles is dominated by diffuse scattering that is indicative of short-range order and cannot be indexed to the defective structural models generated. In addition to the low atomic scattering factor of titanium, these results confirm the need for high-resolution electron microscopy methods for modelling nanoscale disorder in titanium MOFs.

Synthesis of MUV-10 in sub-stoichiometric linker conditions favours the formation of missing cluster vacancies for direct impact in their porosity.     

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R² from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

Machine learning models of the morphology and surface activity of natural clays enable multiobjective design of functional materials, e.g. acid nanocatalyzers.     

Design of Experiment (DoE) as a Tool for the Optimization of Source Conditions in SEC‐ESI‐MS of Functional Synthetic Polymers Synthesized via ATRP

Design of experiment (DoE) is applied to establish the optimum ionization conditions for analyzing synthetic polymers via coupled size exclusion chromatography electrospray ionization mass spectrometry (SEC‐ESI‐MS) yielding maximum ionization efficiency. The ion source conditions were optimized with regard to the ionization efficiency, the amount of fragmentation, as well as the formation of salt adducts. A D ‐optimal experimental design was employed for this purpose and the recorded data were evaluated by a quadratic response surface model, accounting for possible interactions between the individual source settings. It was established that the ionization efficiency can be improved by up to one order of magnitude without compromising the softness of the ionization process and that optimal ionization conditions are found at similar source settings regardless of the charge state. The present optimization exercise therefore provides a hands‐on guide for the use of experimental design to determine optimum ionization conditions during the SEC‐ESI‐MS of functional polymers.

     

Structure–function relationships in aryl diazirines reveal optimal design features to maximize C–H insertion

Diazirine reagents allow for the ready generation of carbenes upon photochemical, thermal, or electrical stimulation. Because carbenes formed in this way can undergo rapid insertion into any nearby C–H, O–H or N–H bond, molecules that encode diazirine functions have emerged as privileged tools in applications ranging from biological target identification and proteomics through to polymer crosslinking and adhesion. Here we use a combination of experimental and computational methods to complete the first comprehensive survey of diazirine structure–function relationships, with a particular focus on thermal activation methods. We reveal a striking ability to vary the activation energy and activation temperature of aryl diazirines through the rational manipulation of electronic properties. Significantly, we show that electron-rich diazirines have greatly enhanced efficacy toward C–H insertion, under both thermal and photochemical activation conditions. We expect these results to lead to significant improvements in diazirine-based chemical probes and polymer crosslinkers.

Electron-rich aryl diazirines have lower activation temperatures and a longer λ_max than electron-poor analogues, and undergo C–H insertion up to ten-fold more efficiently—suggesting improved performance for biological probes and polymer crosslinkers.     

Ruthenaelectro-catalyzed C–H acyloxylation for late-stage tyrosine and oligopeptide diversification

Ruthenaelectro(ii/iv)-catalyzed intermolecular C–H acyloxylations of phenols have been developed by guidance of experimental, CV and computational insights. The use of electricity bypassed the need for stoichiometric chemical oxidants. The sustainable electrocatalysis strategy was characterized by ample scope, and its unique robustness enabled the late-stage C–H diversification of tyrosine-derived peptides.

Ruthenaelectro(ii/iv)-catalyzed intermolecular C–H acyloxylations of oligopeptides have been developed by the guidance of key experimental, CV and computational insights.     

Directed Markovnikov hydroarylation and hydroalkenylation of alkenes under nickel catalysis

We report a full account of our research on nickel-catalyzed Markovnikov-selective hydroarylation and hydroalkenylation of non-conjugated alkenes, which has yielded a toolkit of methods that proceed under mild conditions with alkenyl sulfonamide, ketone, and amide substrates. Regioselectivity is controlled through catalyst coordination to the native Lewis basic functional groups contained within these substrates. To maximize product yield, reaction conditions were fine-tuned for each substrate class, reflecting the different coordination properties of the directing functionality. Detailed kinetic and computational studies shed light on the mechanism of this family of transformations, pointing to transmetalation as the turnover-limiting step.

Native Lewis basic functional groups enable the nickel-catalyzed Markovnikov-selective hydroarylation and hydroalkenylation of unactivated alkenes with organoboron reagents.     

Chromatographic Behavior of Fosinopril Sodium and Fosinoprilat Using Neural Networks

In this paper, the chromatographic characterization of fosinopril sodium and fosinoprilat is presented. The first stept was pK _a determination for the active substance and its degradation product using RP-LC. It was followed by optimization employing the combination of experimental design and artificial neural networks. For the definition of input and output variables, the central composite design for three factors was built. Back propagation algorithm was applied to model the system, and then the optimization of the experimental conditions was carried out in the neural network with 3-8-2 structure, which confirmed to be able to provide the maximum performance. From the method optimization, the most appropriate experimental conditions for fosinopril sodium and fosinoprilat analysis were extracted. The optimized method was validated and applied in the quality control of tablets and for forced degradation studies.     

Single-chain magnet behavior in a finite linear hexanuclear molecule

The careful monitoring of crystallization conditions of a mixture made of a Tb^III building block and a substituted nitronyl-nitroxide that typically provides infinite coordination polymers (chains), affords a remarkably stable linear hexanuclear molecule made of six Tb^III ions and five NIT radicals. The hexanuclear units are double-bridged by water molecules but ab initio calculations demonstrate that this bridge is inefficient in mediating any magnetic interaction other than a small dipolar antiferromagnetic coupling. Surprisingly the hexanuclears, despite being finite molecules, show a single-chain magnet (SCM) behavior. This results in a magnetic hysteresis at low temperature whose coercive field is almost doubled when compared to the chains. We thus demonstrate that finite linear molecules can display SCM magnetic relaxation, which is a strong asset for molecular data storage purposes because 1D magnetic relaxation is more robust than the relaxation mechanisms observed in single-molecule magnets (SMMs) where under-barrier magnetic relaxation can operate.

A stable hexanuclear molecule made of a Tb^III building block and a substituted nitronyl-nitroxide radical show a single-chain magnet behavior despite being a finite molecule.