Molecular Motifs Research Articles

Responsive Chirality: Tailoring Supramolecular Assemblies with External Stimuli as Future Platforms for Electronic/ Spintronic Materials.

Supramolecular chirality is the major branch of supramolecular chemistry, which not only plays important roles in biological processes but also in synthetically designed aggregated systems. To understand the complex processing of biological systems, the only way is to design supramolecular chiral ensembles that mimic natural biomolecules such as Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), amino acids, etc. In addition, chiral systems and self-assemblies as molecular motifs with breaking spatial inversion symmetry have been regarded as key substances in electronics and spintronics as well as in fundamental chemistry and physics. Here, in this review, our major concern is understanding modulation in spatial arrangements and packing modes under the impact of any external stimuli, which results in tailoring the handedness of resulted supramolecular chiral superstructures. We, in this review, highlighted the role of external stimuli such as solvent, chemical additives, photo exposure, etc. in altering the supramolecular chirality for their future utility as "active switches" in optoelectronic and spintronic devices and applications.

Interorbital antisymmetric hopping generated flat bands on kagome and pyrochlore Lattices

Flat bands are intriguing platforms for correlated and topological physics. Various methods have been developed to create flat bands utilizing lattice geometry, but the investigation of orbital symmetry in multiorbital materials is a new area of focus. Here, we introduce a site symmetry-based approach to emerging multiorbital 2D and 3D flat bands on the kagome and pyrochlore lattices. As a conceptual advance, the one-orbital flat bands are shown to originate as mutual eigenstates of isolated molecular motifs. Further developing the mutual eigenstate method for multiple orbitals transforming differently under the site symmetries, we derive interorbital hopping generated flat bands from the antisymmetric interorbital Hamiltonian and introduce group-theoretic descriptions of the flat band wavefunctions. Realizations of multiorbital flat bands in realistic materials are shown to be possible in the Slater-Koster formalism. Our findings provide new directions for exploring flat-band electronic structures for novel correlated and topological quantum states.

Modeling of graphene like structure by utilizing phenanthrocarbazole for tailoring the second-order nonlinear optical properties

Lower band gap with excellent intramolecular charge transfer plays a pivotal role towards developing the strong nonlinear optical (NLO) properties in a molecule. In the current investigation, we have designed a series of graphene like molecules with promising organic fragments that we have strategically adjoin the phenanthrocarbazole and tetracyanobuta-1,3-dienes (TCBD) fragment. Density functional theory (DFT) approach with CAM-B3LYPP/6-311+G(d,p) were performed to evaluate the nonlinear optical properties of designed derivative. The NLO characteristics were analysed the static polarizability (α0), first order hyperpolarizabilities (βtot) and band gap. The calculated results revealed that the designed molecules have better NLO response and highest βtot were observed for molecule 10 (117871 au). Time dependent density functional based theories at the same level of theory were also performed to calculate the absorption spectra. The calculated results observed that there was a red shift trend with reduced band gap of designed derivative. Further, to substantiate our finding, we have also calculated density of states (DOS), molecular electrostatic potential (MESP) and transition density matrix (TDM) at the same level of theory. As a consequence the designed derivative shed light that the efficient TCBD group with graphene like molecular system and donor group can be utilized to design new molecular motif for NLO applications.

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug–target binding affinity prediction

Accurate prediction of drug–target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds, thereby mitigating labor and financial losses. While graph neural networks (GNNs) have been applied to DTA, existing GNNs have limitations in effectively extracting substructural features across various sizes. Functional groups play a crucial role in modulating molecular properties, but existing GNNs struggle with feature extraction from certain motifs due to scale mismatches. Additionally, sequence-based models for target proteins lack the integration of structural information. To address these limitations, we present SSR-DTA, a multi-layer graph network capable of adapting to diverse structural sizes, which can extract richer biological features, thereby improving the robustness and accuracy of predictions. Multi-layer GNNs enable the capture of molecular motifs across different scales, ranging from atomic to macrocyclic motifs. Furthermore, we introduce BiGNN to simultaneously learn sequence and structural information. Sequence information corresponds to the primary structure of proteins, while graph information represents the tertiary structure. BiGNN assimilates richer information compared to sequence-based methods while mitigating the impact of errors from predicted structures, resulting in more accurate predictions. Through rigorous experimental evaluations conducted on four benchmark datasets, we demonstrate the superiority of SSR-DTA over state-of-the-art models. Particularly, in comparison to state-of-the-art models, SSR-DTA demonstrates an impressive 20% reduction in mean squared error on the Davis dataset and a 5% reduction on the KIBA dataset, underscoring its potential as a valuable tool for advancing DTA prediction.

Supramolecular scaffold-directed two-dimensional assembly of pentacene into a configuration to facilitate singlet fission.

Molecular assemblies featuring two-dimensionality have attracted increasing attention, whereas such structures are difficult to construct simply relying on spontaneous molecular assembly. Here, we present two-dimensional assemblies of acene chromophores achieved using a tripodal triptycene supramolecular scaffold, which have been shown to exhibit a strong ability to assemble molecular and polymer motifs two-dimensionally. We designed pentacene and anthracene derivatives sandwiched by two triptycene units. These compounds assemble into expected two-dimensional structures, with the pentacene chromophores having both sufficient overlap to cause singlet fission and space for conformational change to facilitate the dissociation of a triplet pair into free triplets, which is not the case for the anthracene analog. Detailed spectroscopic analysis revealed that the pentacene chromophore in the assembly undergoes singlet fission with a quantum yield of 88 ± 5%, giving rise to triplet pairs, from which free triplets are efficiently generated (ΦT = 130 ± 8.8%). This demonstrates the utility of the triptycene-based scaffold to design functional π-electronic molecular assemblies.

A Chemical Toolbox to Unveil Synthetic Nature-Inspired Antifouling (NIAF) Compounds.

The current scenario of antifouling (AF) strategies to prevent the natural process of marine biofouling is based in the use of antifouling paints containing different active ingredients, believed to be harmful to the marine environment. Compounds called booster biocides are being used with copper as an alternative to the traditionally used tributyltin (TBT); however, some of them were recently found to accumulate in coastal waters at levels that are deleterious for marine organisms. More ecological alternatives were pursued, some of them based on the marine organism mechanisms' production of specialized metabolites with AF activity. However, despite the investment in research on AF natural products and their synthetic analogues, many studies showed that natural AF alternatives do not perform as well as the traditional metal-based ones. In the search for AF agents with better performance and to understand which molecular motifs were responsible for the AF activity of natural compounds, synthetic analogues were produced and investigated for structure-AF activity relationship studies. This review is a comprehensive compilation of AF compounds synthesized in the last two decades with highlights on the data concerning their structure-activity relationship, providing a chemical toolbox for researchers to develop efficient nature-inspired AF agents.

Powder-diffraction-based structural comparison for crystal structure prediction without prior indexing

The objective of crystal structure prediction (CSP) is to predict computationally the thermodynamically stable crystal structure of a compound from its stoichiometry or its molecular diagram. Crystal similarity indices measure the degree of similarity between two crystal structures and are essential in CSP because they are used to identify duplicates. Powder-based indices, which are based on comparing X-ray diffraction patterns, allow the use of experimental X-ray powder diffraction data to inform the CSP search. Powder-assisted CSP presents two unique difficulties: (i) the experimental and computational structures are not entirely comparable because the former is subject to thermal expansion from lattice vibrations, and (ii) experimental patterns present features (noise, background contribution, varying peak shapes etc.) that are not easily predictable computationally. This work presents a powder-based similarity index (GPWDF) based on a modification of the index introduced by de Gelder, Wehrens & Hageman [J. Comput. Chem. (2001), 22, 273–289] using cross-correlation functions that can be calculated analytically. Based on GPWDF, a variable-cell similarity index (VC-GPWDF) is also proposed that assigns a high similarity score to structures that differ only by a lattice deformation and which takes advantage of the analytical derivatives of GPWDF with respect to the lattice parameters. VC-GPWDF can be used to identify similarity between two computational structures generated using different methods, between a computational and an experimental structure, and between two experimental structures measured under different conditions (e.g. different temperature and pressure). VC-GPWDF can also be used to compare crystal structures with experimental patterns in combination with an automatic pre-processing step. The proposed similarity indices are simple, efficient and fully automatic. They do not require indexing of the experimental pattern or a guess of the space group, they account for deformations caused by varying experimental conditions, they give meaningful results even when the experimental pattern is of very poor quality, and their computational cost does not increase with the flexibility of the molecular motif.

Molecular Understanding of Li Metal Anode

Developing new liquid electrolyte is a promising approach to enable the next-generation high-energy Li metal battery for wide-ranging application such as electric vehicles (EV). However, the high reactivity of Li metal interface renders it crucial to form favorable solid-electrolyte interphase (SEI), via active electrolyte decomposition, for highly reversible operation of Li-metal anode (LMA). Therefore, deeply understanding how the molecular structures of electrolyte species precisely control both the interfacial reactivity and the operational reversibility of LMA, can further guide and inspire new electrolyte systems with progressively improved battery performance.Herein, we systematically investigated the impact of Li salts with different imide anions dissolved in ether-based solvent, on the resulting Coulombic efficiencies (CE) of LMA, whereas beneficial and detrimental molecular motifs were first identified. By leveraging the non-washing XPS protocol newly developed at Stanford, we managed to uncover the distinct molecular reactivity leading to reductive bond cleavage as well as new bond formation at Li-metal potential. Such nanoscale reactivity of SEI-forming reactions was discovered to profoundly govern the macroscopic performance of LMA, which simultaneously emphasized the significance of electric double layer (EDL). Importantly, aided by gas-titration experiments to quantify the by-products in SEI, we revealed the critical molecular origins of irreversible capacity loss of LMA during continuous Li-metal plating and stripping. These new insights allow us to formulate rational strategies of molecular engineering to design LMA interface with high reversibility and minimal side reactivity.

Generation of pyridinylethylidenemalononitrile cyclic dimer: A facile route to multi-stimuli-responsive far-red/NIR luminogens

A simple synthetic strategy was successfully employed to develop NIR luminophores with switchable color-contrasting luminescence properties. D-A molecular motifs with dicyanovinylene appended pyridinyl moiety as the electron-withdrawing unit was not reported among luminescent molecules so far, owing to its unstable precursor compound pyridinylethylidenemalononitrile. By modifying the Knoevenagel condensation reaction pathways, the present work succeeded for the first time in identifying and crystallizing a novel cyclic dimerization product of the precursor compound. It further reports a new, simple, efficient synthetic route to achieve push-pull far-red/NIR emissive luminophores with the dicyanovinylene appended pyridinyl skeleton as the potent electron withdrawing unit. The enhanced charge transfer and the acidochromic characteristics make them suitable candidates in multi-stimuli-responsive smart materials with anti-counterfeiting applications.

A high-throughput assay identifies molecules with antimicrobial activity against persister cells.

Introduction. Persister cells are transiently non-growing antibiotic-tolerant bacteria that cause infection relapse, and there is no effective antibiotic therapy to tackle these infections.Gap statement. High-throughput assays in drug discovery are biased towards detecting drugs that inhibit bacterial growth rather than killing non-growing bacteria. A new and simple assay to discover such drugs is needed.Aim. This study aims to develop a simple and high-throughput assay to identify compounds with antimicrobial activity against persister cells and use it to identify molecular motifs with such activity.Methodology. We quantified Staphylococcus aureus persister cells by enumeration of colony forming units after 24 h ciprofloxacin treatment. We first quantified how the cell concentration, antibiotic concentration, growth phase and presence/absence of nutrients during antibiotic exposure affected the fraction of persister cells in a population. After optimizing these parameters, we screened the antimicrobial activity of compound fragments to identify molecular structures that have activity against persister cells.Results. Exponential- and stationary-phase cultures transferred to nutrient-rich media displayed a bi-phasic time-kill curve and contained 0.001-0.07% persister cells. A short rifampicin treatment resulted in 100% persister cells for 7 h, after which cells resumed activity and became susceptible. Stationary-phase cultures displayed a low but constant death rate but ultimately resulted in similarly low survival rates as the exponential-phase cultures after 24 h ciprofloxacin treatment. The persister phenotype was only maintained in most of the population for 24 h if cells were transferred to a carbon-free minimal medium before exposure to ciprofloxacin. Keeping cells starved enabled the generation of high concentrations of S. aureus cells that tolerate 50× MIC ciprofloxacin, and we used this protocol for rapid screening for biocidal antibiotics. We identified seven compounds from four structural clusters with activity against antibiotic-tolerant S. aureus. Two compounds were moderately cytotoxic, and the rest were highly cytotoxic.Conclusion. Transferring a stationary-phase culture to a carbon-free minimal medium for antimicrobial testing is a simple strategy for high-throughput screening for new antibiotics that kill persister cells. We identified molecule fragments with such activity, but further screening is needed to identify motifs with lower general cytotoxicity.

Unraveling the unique structural motifs of glucuronoxylan from hybrid aspen wood

Xylan is a fundamental structural polysaccharide in plant secondary cell walls and a valuable resource for biorefinery applications. Deciphering the molecular motifs of xylans that mediate their interaction with cellulose and lignin is fundamental to understand the structural integrity of plant cell walls and to design lignocellulosic materials. In the present study, we investigated the pattern of acetylation and glucuronidation substitution in hardwood glucuronoxylan (GX) extracted from aspen wood using subcritical water and alkaline conditions. Enzymatic digestions of GX with β-xylanases from glycosyl hydrolase (GH) families GH10, GH11 and GH30 generated xylo-oligosaccharides with controlled structures amenable for mass spectrometric glycan sequencing. We identified the occurrence of intramolecular motifs in aspen GX with block repeats of even glucuronidation (every 2 xylose units) and consecutive glucuronidation, which are unique features for hardwood xylans. The acetylation pattern of aspen GX shows major domains with evenly-spaced decorations, together with minor stretches of highly acetylated domains. These heterogenous patterns of GX can be correlated with its extractability and with its potential interaction with lignin and cellulose. Our study provides new insights into the molecular structure of xylan in hardwood species, which has fundamental implications for overcoming lignocellulose recalcitrance during biochemical conversion.

De novo DNA-based catch bonds.

All primary chemical interactions weaken under mechanical stress, which imposes fundamental mechanical limits on the materials constructed from them. Biological materials combine plasticity with strength, for which nature has evolved a unique solution-catch bonds, supramolecular interactions that strengthen under tension. Biological catch bonds use force-gated conformational switches to convert weak bonds into strong ones. So far, catch bonds remain exclusive to nature, leaving their potential as mechanoadaptive elements in synthetic systems untapped. Here we report the design and realization of artificial catch bonds. Starting from a minimal set of thermodynamic design requirements, we created a molecular motif capable of catch bonding. It consists of a DNA duplex featuring a cryptic domain that unfolds under tension to strengthen the interaction. We show that these catch bonds recreate force-enhanced rolling adhesion, a hallmark feature of biological catch bonds in bacteria and leukocytes. This Article introduces catch bonds into the synthetic domain, and could lead to the creation of artificial catch-bonded materials.

Tuning Molecular Packing and Boosting Self-Assembling Properties via Ring Fusion Strategy in Naphthalimide-Based A-D-A Conjugated Systems.

Two fully fused acceptor-donor-acceptor (A-D-A) architecture conjugated derivatives (NPF and NCF) comprising an electron-withdrawing naphthalimide (NMI) and two different electron-donating cores, phenanthrene and carbazole, respectively, were conveniently synthesized by bismuth(III)-catalyzed selective cyclization of vinyl ethers. Compared with their corresponding single bond-linked A-D-A molecules NPS and NCS, both having a moderately twisted aromatic configuration, the ring fusion strategy leads to fully coplanar conjugated skeletons and greatly changes the electronic structures, photophysical properties, self-assembling behaviors, and molecular packing motifs. In particular, the naphthalimide/carbazole derivative NCF exhibits intriguing 2D brickwork packing and significantly enhanced self-assembling properties.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Interrupted Corey-Chaykovsky Reaction of Tethered Bis-Enones to Access 2,3-Epoxy-hexahydrofluoren-9-ones.

The Corey-Chaykovsky reaction is usually employed to synthesize cyclopropanes from activated olefins. We intercepted the intermediates prevailing during this transformation and diverted the process for the creation of intricate molecular motifs. We describe an unusual skeletal remodelling of tethered bis-enones to 2,3-epoxy-hexahydrofluoren-9-ones via an "interrupted Corey-Chaykovsky reaction". The strategy rationally merges the nucleophilic features of sulfur ylides with electronically biased olefins to achieve the regio- and stereoselective synthesis of several new classes of hydrofluorenones. We have demonstrated the synthetic utility of the products in accessing several highly functionalized molecules.

A Difluoro-Methoxylated Ending-Group Asymmetric Small Molecule Acceptor Lead Efficient Binary Organic Photovoltaic Blend.

Developing a new end group for synthesizing asymmetric small molecule acceptors (SMAs) is crucial for achieving high-performance organic photovoltaics (OPVs). Herein, an asymmetric small molecule acceptor, BTP-BO-4FO, featuring a new difluoro-methoxylated end-group is reported. Compared to its symmetric counterpart L8-BO, BTP-BO-4FO exhibits an upshifted energy level, larger dipole moment, and more sequential crystallinity. By adopting two representative and widely available solvent additives (1-chloronaphthalene (CN) and 1,8-diiodooctane (DIO)), the device based on PM6:BTP-BO-4FO (CN) photovoltaic blend demonstrates a power conversion efficiency (PCE) of 18.62% with an excellent open-circuit voltage (VOC) of 0.933V, which surpasses the optimal result of L8-BO. The PCE of 18.62% realizes the best efficiencies for binary OPVs based on SMAs with asymmetric end groups. A series of investigations reveal that optimized PM6:BTP-BO-4FO film demonstrates similar molecular packing motif and fibrillar phase distribution as PM6:L8-BO (DIO) does, resulting in comparable recombination dynamics, thus, similar fill factor. Besides, it is found PM6:BTP-BO-4FO possesses more efficient charge generation, which yields better VOC-JSC balance. This study provides a new ending group that enables a cutting-edge efficiency in asymmetric SMA-based OPVs, enriching the material library and shed light on further design ideas.

Bulk transparent supramolecular glass enabled by host–guest molecular recognition

Supramolecular glass is a non-covalently cross-linked amorphous material that exhibits excellent optical properties and unique intrinsic structural features. Compared with artificial inorganic/organic glass, which has been extensively developed, supramolecular glass is still in the infancy stage, and itself is rarely recognized and studied thus far. Herein, we present the development of the host–guest molecular recognition motifs between methyl-β-cyclodextrin and para-hydroxybenzoic acid as the building blocks of supramolecular glass. Non-covalent polymerization resulting from the host–guest complexation and hydrogen bonding formation enables high transparency and bulk state to supramolecular glass. Various advantages, including recyclability, compatibility, and thermal processability, are associated with dynamic assembly pattern. Short-range order (host–guest complexation) and long-range disorder (three dimensional polymeric network) structures are identified simultaneously, thus demonstrating the typical structural characteristics of glass. This work provides a supramolecular strategy for constructing transparent materials from organic components.

Synthetic Antifreeze Glycoproteins with Potent Ice-Binding Activity.

Antifreeze glycoproteins (AFGPs) are produced by extremophiles to defend against tissue damage in freezing climates. Cumbersome isolation from polar fish has limited probing AFGP molecular mechanisms of action and limited development of bioinspired cryoprotectants for application in agriculture, foods, coatings, and biomedicine. Here, we present a rapid, scalable, and tunable route to synthetic AFGPs (sAFGPs) using N-carboxyanhydride polymerization. Our materials are the first mimics to harness the molecular size, chemical motifs, and long-range conformation of native AFGPs. We found that ice-binding activity increases with chain length, Ala is a key residue, and the native protein sequence is not required. The glycan structure had only minor effects, and all glycans examined displayed antifreeze activity. The sAFGPs are biodegradable, nontoxic, internalized into endocytosing cells, and bystanders in cryopreservation of human red blood cells. Overall, our sAFGPs functioned as surrogates for bona fide AFGPs, solving a long-standing challenge in accessing natural antifreeze materials.

Designing a New Class of Mechanophoric Polymer Based on Epoxy-Functionalized Rhodamine Derivative.

Mechanophoric polymers are an interesting class of smart polymers which contains a special force-sensitive molecular motif that can lead to a chemical change within the polymer network in response to mechanical force. This investigation reports the design of a mechanophoric polymer based on epoxy-functionalized rhodamine via a monomeric approach. In this case, rhodamine (Rh) is modified with glycidyl methacrylate (GMA) through an epoxy-amine reaction to design a vinyl-functionalized multi-armed macromonomer (Rh-GMA), which is reacted with butyl acrylate (BA) to prepare the crosslinked polymeric film. The crosslinked polymeric film demonstrates mechanophoric properties under UV and stretching conditions.

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy.

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multimodal advantage" over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.