Molecular Systems Design & Engineering最新文献

The estimation of polymer properties is of crucial importance in many domains such as energy, healthcare, and packaging. Recently, graph neural networks (GNNs) have shown promising results for the prediction of polymer properties based on supervised learning. However, the training of GNNs in a supervised learning task demands a huge amount of polymer property data that is time-consuming and computationally/experimentally expensive to obtain. Self-supervised learning offers great potential to reduce this data demand through pre-training the GNNs on polymer structure data only. These pre-trained GNNs can then be fine-tuned on the supervised property prediction task using a much smaller labeled dataset. We propose to leverage self-supervised learning techniques in GNNs for the prediction of polymer properties. We employ a recent polymer graph representation that includes essential features of polymers, such as monomer combinations, stochastic chain architecture, and monomer stoichiometry, and process the polymer graphs through a tailored GNN architecture. We investigate three self-supervised learning setups: (i) node- and edge-level pre-training, (ii) graph-level pre-training, and (iii) ensembled node-, edge- & graph-level pre-training. We additionally explore three different transfer strategies of fully connected layers with the GNN architecture. Our results indicate that the ensemble node-, edge- & graph-level self-supervised learning with all layers transferred depicts the best performance across dataset size. In scarce data scenarios, it decreases the root mean square errors by 28.39% and 19.09% for the prediction of electron affinity and ionization potential compared to supervised learning without the pre-training task.

Electrowetting display (EWD) technology is among the most promising reflective display technologies due to its full-color capabilities and fast video-speed performance. The colored EWD inks are typically prepared by dissolving soluble organic dyes in non-polar solvents, which significantly influence the color performance, electro-optical behaviour, and longevity of EWD devices. In this study, density functional theory (DFT) at the PBE1PBE/6-31G* level and time-dependent density functional theory (TD-DFT) at the M06-2X/6-31G* level were utilized to calculate a series of benzobisthiadiazole-based donor–acceptor–donor (D–A–D) type near-infrared organic dyes for EWDs, providing structural and spectral data to aid in spectral assignment. The quantum chemical calculations' results align with our experimental synthesis data, showing molecular colors spanning blue, green, and cyan. Detailed investigations into the properties of these dyes, including absorption, electro-optical response, and photo-stability, were conducted. The experimental outcomes indicate that these organic dyes are excellent candidates for EWD applications.

The development of hole transport materials with desirable properties is important for the fabrication of efficient organic light-emitting diodes (OLEDs). The present work demonstrates an approach for developing a library of phenothiazine-based hole transport materials (HTMs) for OLED application with considerably good triplet energy (theoretical). Furthermore, the single-crystal structure analysis at the molecular level for some of the developed molecules reveals the possibility of poor electronic communications between the corresponding units. Theoretical studies on transition dipole orientation revealed that all the present phenothiazine-based molecules have appreciable transition dipole orientation. Hence, the objective of the current work has been to assess the impact of chemical structures on certain features of a group of phenothiazine-based functional molecular HTMs with donor–acceptor characteristics. Finally, the hole-only devices (HODs) were fabricated with the synthesized materials as HTMs, and these showed an enhancement in current density with the increase in operating voltage from ∼2–8 V. All these theoretical and experimental outcomes suggested that the present set of molecules could be used as possible efficient HTMs for OLED applications.

Porous nanostructures exhibit remarkable nanoplatforms for payload delivery to diseased cells with high loading capacity, favorable release profiles, improved hemocompatibility, biocompatibility, and safe clearance after biodegradation. Metal–organic frameworks (MOFs), periodic mesoporous organosilica (PMO), or biodegradable periodic mesoporous organosilica (BPMO) epitomize a similar category of structured and crystalline porous coordinated compounds or nanocomposites. Additionally, their elevated surface-to-volume ratio, customizable porous configurations, and convenient attachment of favorable ligands to the central metal ions enhance drug loading and release, further demonstrating their potential for drug delivery applications. This review focuses on these materials, including Fe-MOFs, Cu-MOFs, Zr-MOFs, PMO and BPMO, along with multicompartmental mesoporous nanostructures, detailing their specific engineering, chemistry, and optimal drug delivery applications.

α-Synuclein (αSYN), and its tendency to self-aggregate, plays an important role in the development of Parkinson's disease (PD). αSYN aggregates are characterized by a stacking of αSYN chains and an interaction between the stackings to form dimer-like structures. The stability of these supramolecular assemblies is ensured by the presence of numerous residues that adopt “β-strand” and then “β-sheet” conformations, implying multiple interactions within and between the chains of αSYN. Following our previous study on the ability of small organic molecules to form columnar supramolecular assemblies (organic nanotubes, ONs) [Le Bras, L.; Dory, Y. L.; Champagne, B. Computational prediction of the supramolecular self-assembling properties of organic molecules: the role of conformational flexibility of amide moieties. Phys. Chem. Chem. Phys., 2021, 23, 20453–20465], we propose here to unravel the ability of these ONs to interact with αSYN aggregates. More than an interaction, we expect the organic molecules to avoid the complete aggregation process and ideally to induce destabilization of the stacking. Both molecular dynamics simulation and quantum mechanical-based calculations are used to identify the key parameters of the interaction and the resulting (de)stabilization of the assembly.

Bulk radical copolymerisation of N-vinylcaprolactam (VCL) and N-vinylimidazole (VI) is studied experimentally and theoretically. It is shown that the copolymer composition is maintained up to high comonomer conversions. This is explained by a constant ratio of concentrations of comonomers in the reaction zone. The copolymers obtained show thermally induced conformational behaviour. In an aqueous medium above 60 °C, they can form compact globular structures with a hydrophobic core of VCL monomer units covered by a hydrophilic corona of VI monomer units, which allows them to be considered as a basis for thermally switchable functional nanostructures.

Aptamers are short single-stranded oligonucleotides, which offer several advantages over antibodies as bioreceptors. The widely used method for generating aptamer sequences, SELEX, has some limitations such as a limited oligonucleotide library used and amplification bias of PCR. Bioinformatics approaches have been shown to optimise and increase aptamer affinity. This research aimed to enhance the affinity of the NT-proBNP (N-terminal pro-brain natriuretic peptide, a biomarker for heart failure)-targeting aptamer acquired from SELEX using computational strategies involving sequence truncation and secondary structure-guided random mutations. DNA aptamers and protein structures are predicted by MC-Fold + 3dDNA and Robetta, respectively, whereas the computational evaluations utilize molecular docking, interaction profiles, and molecular dynamics simulations. The structural and energetic analysis revealed that the in silico optimised aptamer had more stable and robust interactions in binding to the NT-proBNP protein than the SELEX-obtained aptamer. Furthermore, our approach was supported and confirmed by in vitro colourimetric assay based on gold nanoparticle aggregation, evidenced by a detection limit of 0.5 ng mL⁻¹ which is lower than the SELEX-obtained aptamer (2.3 ng mL⁻¹).

ZSM-48 is a kind of high-silica zeolite with one dimensional (1D) 10-member ring (10-MR) channel structure. It is well known for its unique pore structure and acid properties, as well as exceptional catalytic performance in various reactions. However, the diffusion limitation and insufficient acid density pose significant challenges to its widespread application and promotion. This review aims to summarize the advancements in enhancing diffusivity and regulating acid properties of ZSM-48 zeolite, as well as its catalytic applications. To alleviate diffusion limitations, the construction of hierarchical ZSM-48 zeolites through post-treatment and in situ strategies is extensively summarized. Ongoing endeavors focus on determining the optimal balance between maintaining structural integrity and improving mass transfer capacity through post-treatment techniques. Concerning acid regulation, various strategies such as the use of a special organic structure directing agent (OSDA), seed-assisted synthesis, zeolite hybridization, and heteroatom doping strategies have been developed. The emphasis on acid regulation in ZSM-48 zeolite involves efforts to design or discover more cost-effective OSDAs. Additionally, researchers are exploring simpler and more economical seed-assisted synthesis routes to produce Al-rich candidates. In terms of catalytic application, extensive research has been conducted on various reactions including hydroisomerization of paraffin, isomerization of xylenes, cracking of hydrocarbons, and methanol conversion to hydrocarbons. Its distinctive catalytic performance is primarily related to the shape-selective advantage conferred by its 1D channel structure. In particular, ZSM-48 zeolite is widely regarded as the leading candidate in paraffin hydroisomerization reactions, attributed to its high proportion of multi-branched isomers in the catalytic products. The present review aims to provide a comprehensive reference for researchers dedicated to the synthesis, modification, and application of ZSM-48 zeolite.

The emerging variants of SARS-CoV-2 have raised serious concerns worldwide due to their infectivity, lethality, and unpredictability. Moreover, the ability of these variants to bypass vaccine protection and immunity has compelled the research community to design novel compounds against SARS-CoV-2. This study focuses on designing novel molecules using artificial intelligence methods for the development of new therapeutics against SARS-CoV-2. Furthermore, these molecules were validated against main protease (M^pro) using in-silico methods. In this study, we used the DeepScreening RNN-based web server to design novel molecules using potential inhibitors of M^pro from CHEMBL4495582. Screened compounds were further validated by molecular docking and molecular dynamics (MD) simulation studies. One hundred molecules were obtained and studied through molecular docking and MD simulations. Additionally, eight molecules, based on their docking scores, were also evaluated for electronic structure properties by conducting Density Functional Theory (DFT) calculations using the B3LYP method and a 6-31G basis set. A total of three compounds, namely L18, L36, and L26, showed very good binding and stability with the active site of the M^pro protein. The results of this study demonstrate that potential molecules can be designed using artificial intelligence methods for the rapid development of drug candidates against SARS-CoV-2, addressing the alarming worldwide situation of emerging deadly SARS-CoV-2 variants. We hope that our study will attract the attention of the scientific community to increase the application of artificial intelligence techniques in the drug discovery process.

Blue TADF materials demonstrate significant potential for OLED and photovoltaic applications. Nevertheless, systematic studies are essential to explore the relationship between molecular structures and luminescence properties to develop blue-TADF emitters. In this study, a series of new 24 donor–acceptor–donor (D–A–D) type molecules with different electron donors and acceptors are designed theoretically, and their photophysical properties are analyzed by using DFT and TD-DFT methods. We examined the combined impact of sulfur oxidation and the symmetric incorporation of a nitrogen heteroatom, with positional modifications (2-dipyridyl and 3-dipyridyl), within the phenyl ring of the acceptor group. The findings suggest that enhancing both the donating and accepting strength of the molecules results in an orthogonal geometry and a small ΔE_ST, accompanied by an enhanced charge-transfer (CT) character. Upon sulfur oxidation, the magnitude of SOC decreases, resulting in a reduction of ΔE_ST attributed to screening and lone pair effects. Through quantum chemical calculations, we have theoretically identified 12 promising blue TADF molecules, featuring small ΔE_ST, increased SOC magnitude, and higher RISC (∼10⁺⁰⁷ s⁻¹) rates. Overall, our current study provides a robust molecular design approach and reliable computational method for designing a blue TADF emitter.