Molecular Systems Design & Engineering最新文献

DNA origami nanocarriers offer programmable architectures for targeted molecular delivery, yet the interplay between lock design, linker composition, and global conformation remains poorly understood. Here, we employ coarse-grained oxDNA and hybrid ANM–oxDNA simulations to elucidate how these structural parameters collectively govern the stability and release dynamics of a thrombin-loaded DNA nanocarrier. Systematic temperature-dependent simulations reveal that lock stability scales strongly with duplex length—short locks (3–4 bp) dissociate spontaneously under mild thermal conditions, whereas longer locks (≥6 bp) remain tightly closed, requiring external activation for release. The kinetics of cargo release follow a thermally activated profile, with release times decreasing exponentially with temperature and reflecting cooperative effects among multiple linkers. Sequence composition further modulates this behavior: A–T linkers destabilize readily through weak bonding and slip, C–G linkers are stronger but prone to misalignment, and mixed linkers suppress slip to achieve maximal stability. Together, these findings provide a coarse-grained framework for the rational design of DNA-based nanocarriers, highlighting how lock length, sequence composition, and collective linker mechanics can be tuned to achieve thermally responsive yet mechanically robust DNA–protein assemblies.

Ease of preparation and the alkaline stability characteristics have spurred immense interest in polyelectrolytes composed of poly(benzimidazolium) (HMT-PMBI) and poly(bis-arylimidazolium) (PAImMM) ionene anion exchange membranes. The present work emphasises the structure and morphological traits of hydrated structures based on ionene membranes using molecular dynamics simulations. The influence of the benzimidazolium group and the bis-arylimidazolium group on the hydrated structure of the membrane is analysed through the radial distribution function and structure factor at 300 and 333 K. A notable difference is seen in the spatial density maps with an increase in temperature for HMT-PMBI, where a more dense and enhanced distribution of water molecules is shown compared to 300 K. Angular autocorrelation (torsional and rotational) decays relative faster for PAImMM compared to HMT-PMBI. Water clusters around the skewed and extended configurations show a similar hydration pattern for both membranes. The mobility of the hydroxide ion is found to be relatively faster in HMT-PMBI compared to PAImMM. This work provides valuable atomistic insights that can inform the rational design of future ionene derivatives with enhanced ionic conductivity and structural robustness for electrochemical energy applications.

Understanding and predicting molecular properties remains a central challenge in scientific machine learning, especially when training data are limited or task-specific supervision is scarce. We introduce the molecular equivariant transformer (MET), a symmetry-aware pretraining framework that leverages quantum-derived atomic charge distributions to guide molecular representation learning. MET combines an equivariant graph neural network (EGNN) with a transformer architecture to extract physically meaningful features from three-dimensional molecular geometries. Unlike previous models that rely purely on structural inputs or handcrafted descriptors, MET is pretrained to predict atomic partial charges, which are quantities grounded in quantum chemistry. This enables MET to capture essential electronic information without requiring downstream labels. We show that this pretraining scheme improves performance across diverse molecular property prediction tasks, particularly in low-data regimes. Analyses of the learned representations reveal chemically interpretable structure–property relationships, including the emergence of functional group patterns and smooth alignment with molecular dipoles. Ablation studies confirm that the EGNN encoder plays a crucial role in capturing transferable spatial features, while the transformer layers adapt these features to specific prediction tasks. This architecture draws direct analogies to quantum mechanical basis transformations, where MET learns to transition from coordinate-based to electron-based representations in a symmetry-preserving manner. By integrating domain knowledge with modern deep learning techniques, MET offers a unified and interpretable framework for data-efficient molecular modeling, with broad applications in computational chemistry, drug discovery, and materials science.

Computational methods for designing interactions between colloidal particles that induce self-assembly have received much attention for their promise to discover tailored materials. However, it often remains a challenge to translate computationally designed interactions to experiments because they may have features that are too complex, or even infeasible, to physically realize. Toward bridging this gap, we leverage relative-entropy minimization to design pair potentials for binary mixtures of colloidal particles that assemble crystal superlattices. We reduce the dimensionality and extent of the interaction design space by enforcing constraints on the form and parametrization of the pair potentials that are physically motivated by DNA-functionalized nanoparticles. We show that several two- and three-dimensional lattices, including honeycomb and cubic diamond, can be assembled using simple interactions despite their complex structures. We also find that the initial conditions used for the designed parameters as well as the assembly protocol play important roles in determining the outcome and success of the assembly process.

The growing demand for sustainable energy sources has intensified interest in biomass conversion technologies such as hydrothermal liquefaction (HTL), which enables the efficient processing of wet biomass into bio-oil without the need for energy-intensive drying. However, the complexity and instability of raw bio-oil significantly limit its direct application as a renewable fuel or chemical feedstock. Bio-oil is also a source of several valuable chemicals such as phenol. The value of bio-oil as a source of phenol can be increased by converting some of the other components in bio-oil into phenol. This work develops a reaction pathway synthesis framework using computer-aided molecular design (CAMD) to increase the quantity of phenol in bio-oil. CAMD was employed to systematically design molecules that can convert target components in bio-oil into phenol through thermodynamically feasible reaction pathways. The developed approach simultaneously solves the thermodynamic feasibility conditions, atomic balances, and structural constraints for potential reactants and products. The design approach involves representing all relevant thermodynamic equations and feasibility criteria based on the molecular groups involved in the reactions. Target compounds for conversion were selected based on their presence in bio-oil and the feasibility of the associated reactions, and multiple feasible reaction pathways were generated. All identified pathways demonstrated negative Gibbs free energy values, confirming their thermodynamic viability. To recover phenol from the bio-oil, solvent screening and selection were performed, identifying acetone as the most suitable extraction solvent based on its solubility characteristics. Overall, this work highlights the critical role of CAMD in designing strategic reaction pathways and provides a foundation for future experimental validation and industrial application in sustainable bio-oil upgrading.

The rapid advancement of organic photovoltaics (OPVs) depends critically on discovering non-fullerene acceptors (NFAs) with finely balanced optoelectronic properties. Yet, identifying optimal NFAs remains challenging due to the vast chemical space and complex, non-linear structure–property relationships. Here, we present a computational framework integrating physics-guided molecular fragmentation, hierarchical clustering, and combinatorial assembly with uncertainty-aware machine learning to accelerate NFA design. Beginning with 257 experimentally reported NFAs, we assembled a synthetically viable library of 500 000 NFAs with acceptor–donor–acceptor (ADA) structures. An evidential message-passing neural network (MPNN) was trained to predict oscillator strength (f), LUMO offset (ΔE_LUMO), absorption maximum (λ_max), and exciton binding energy (E_b), achieving high accuracy with built-in uncertainty quantification. Compared to the training distribution, our pipeline produced a deterministic enrichment of candidates with tightly converged, target-optimized values (f ≥ 1.5, ΔE_LUMO < 0.25 eV, E_b < 0.32 eV), in line with state-of-the-art OPV performance benchmarks. Quantum chemical validation confirmed prediction fidelity, with all deviations within 22%. This unified and interpretable framework provides a scalable route for rational NFA discovery and establishes a generalizable benchmark for machine learning-guided materials design in organic electronics.

Accurate prediction of blood–brain barrier permeability (BBBP) is essential for central nervous system (CNS) drug development. While graph neural networks (GNNs) have advanced molecular property prediction, they often rely on molecular topology and neglect the three-dimensional geometric information crucial for modeling transport mechanisms. This paper introduces the geometric multi-color message-passing graph neural network (GMC-MPNN), a novel framework that enhances standard message-passing architectures by explicitly incorporating atomic-level geometric features and long-range interactions. Our model constructs weighted colored subgraphs based on atom types to capture the spatial relationships and chemical context that govern BBB permeability. We evaluated GMC-MPNN on three benchmark datasets for both classification and regression tasks, using rigorous scaffold-based splitting to ensure a robust assessment of generalization. The results demonstrate that GMC-MPNN consistently outperforms existing state-of-the-art models, achieving superior performance in both classifying compounds as permeable/non-permeable (AUC-ROC of 0.947 and 0.9212) and in regressing continuous permeability values (RMSE of 0.5628, Pearson correlation of 0.6947). An ablation study further quantified the impact of specific atom-pair interactions, revealing that the model's predictive power derives from its ability to learn from both common and rare, but chemically significant, functional motifs. By integrating spatial geometry into the graph representation, GMC-MPNN sets a new performance benchmark and offers a more accurate and generalizable tool for drug discovery pipelines.

Recent publications in reputable journals have frequently contained incorrect citations of World Health Organisation (WHO) and U. S. Environmental Protection Agency (EPA) guidelines and regulations, respectively. We report their occurrence specifically in the sorption-based atmospheric water harvesting space. These works describe interesting systems designed to absorb and harvest water from air, and therein frequently reference the WHO and EPA regarding (primarily) lithium, potassium, sodium, calcium and magnesium concentrations to indicate the purity and safety of the resulting harvested water. These reported guideline values vary wildly and rarely feature a cited source for the values given. The WHO and EPA have never provided primary guidelines or standards for any of these ions. Of particular interest is lithium, which currently has no associated guideline but will be an increasingly common environmental pollutant (with the exponential increase in Li battery usage) and has recently been added to the EPA “anticipated to occur in public water systems” list. We are of the opinion that these citation practices are symptomatic of deeper issues in scientific publishing which will not self-correct, requiring deeper scrutiny from authors, peer reviewers and editorial staff.

A comparative study is carried out to evaluate the performance of conventional linear surfactants (HT) and X-shaped gemini surfactants (GS), as nanocarriers for oily co-solutes. Both surfactant types achieve high oil solubilization efficiency (SE% > 98%) at the different surfactant concentrations and surfactant/oil ratios studied, but their aggregation and transport behavior are markedly different. It is noted that the GS model forms spherical micelles, whose stable aggregation process imparts enhanced mobility to the aggregates, with diffusion coefficients almost twice as large as those shown by the HT systems. In contrast, HT surfactants display a more dynamic aggregation process, limiting transport efficiency, except at high surfactant/oil ratios of 1 : 20, where partial aggregation stabilization occurs. By linking self-assembly features to oil solubilization efficiencies and transport properties, this work highlights the superior nanocarrier capability of gemini surfactants and their significance for advanced soft matter and nanotechnology applications.

Salbutamol (SBM), a β₂-adrenergic agonist commonly prescribed for bronchospasm, is increasingly monitored in sports medicine due to its potential misuse as a performance-enhancing agent. To address the need for rapid, cost-effective, and portable anti-doping diagnostics, a nanozyme-based sensing platform using copper–gallic acid hybrid structures (Cu@GA·HSs) was developed. These nanozymes exhibited oxidase-like activity, enabling sensitive and selective optical detection of SBM across a wide concentration range (125–5000 μg mL⁻¹), with excellent analytical accuracy (96.8–99.8%) and precision (CV < 2.0%). The system demonstrated strong operational stability, retaining 60% catalytic activity after 24 days at 4 °C, and strong resistance to chemical interference, including metal ions and non-polar solvents (selectivity coefficient ≈1). Benchmarking against HPLC revealed excellent agreement (R² = 0.997; deviation <0.06%), validating its analytical performance. Molecular docking and dynamics simulations further revealed specific SBM–matrix interactions underlying the sensor's selectivity and robustness. Together, these results highlight a systems-level approach integrating nanozyme chemistry with computational modeling to engineer next-generation biosensors for anti-doping applications.