Molecular Systems Design & Engineering最新文献

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Flame-retardant thermosetting polymers are extensively used in construction materials and aerospace applications due to their inherent stability and performance characteristics. Traditional processing methods, however, are limited to producing simple geometries such as strips, blocks, and plates. Additionally, small molecule flame retardants exist in the resin matrix in a free form, and as the resin is used over time, these small molecule flame retardants tend to migrate, which deteriorates the flame-retardant performance of the material. Herein, we synthesized a flame retardant containing P and N elements with a double bond, which also serves as a curing agent, through molecular design and applied it in an acrylate–epoxy resin dual-modulus network system. Initial photopolymerization facilitated the creation of a low-modulus acrylate network, endowing the material with significant flexibility and allowing for arbitrary shaping. The double bonds present in the designed flame retardant ensure its integration into the acrylate network during photopolymerization, thereby mitigating migration issues. Subsequently, this flexible material undergoes thermal curing to form a high-modulus epoxy resin network, increasing the material's tensile modulus by up to 2500 times, tensile strength by up to 300 times, and glass transition temperature by up to 180 °C, resulting in a rigid material. Therefore, this work introduces an innovative approach to fabricating flame-retardant thermosetting materials with complex shapes while effectively reducing the migration of flame retardant molecules within the resin matrix.

Antibody-based therapeutics continue to be an important pharmaceutical development modality. Crystallization of antibodies is important for structural characterization, but in addition has the potential for use as a separation method and for use as a dosage form. Nevertheless, bringing about controlled crystallization of an antibody remains a challenging task due to its large size, high degree of segmental flexibility, and the intricacy of all the occurring interactions (e.g., protein–protein interactions, protein–solvent interactions, etc.). Methods to predict important contact sites could help to develop such crystallization methods. However, limited data and understanding have hitherto not allowed the development of such robust methods. This study employs machine learning combined with in silico modelling of crystal structures using available experimental structures to identify the crucial physicochemical features necessary for successful antibody crystallization in an attempt to remedy that gap. The developed method can with good accuracy distinguish crystal-site residues from non-crystal-site residues. A set of 510 descriptors is utilized to characterize each residue, which is treated as a distinct data point. Moreover, new algorithms have been developed to design novel descriptors that improve the model's predictive capabilities. Fragment antigen-binding (Fab) regions are investigated due to the scarcity of full-length monoclonal antibodies (mAbs) crystal structures. The current findings show that the extreme gradient boosting (XGBoost) algorithm effectively identifies crystal site residues, as evidenced by an AUPRC value that is more than 3-fold higher than that of the baseline model. The top-ranked descriptors indicate that crystal-site residues are primarily characterized by solvent-exposed residues with high spatial aggregation propensity (SAP), signifying hydrophobic patches, and their immediate surface-exposed neighbors. Moreover, these high SAP residues are often surrounded by other solvent-exposed residues that are either polar, charged, or both. In contrast, residues not involved in crystal interfaces generally lack these essential features, though some might be excluded due to specific crystal lattice arrangements. Additionally, reducing the feature set from 510 to the top 15% in the XGBoost model yields similar performance while significantly simplifying the model.

Inspired by a diverse array of hierarchical structures and mechanical function in spider silk, we leverage building blocks that can form non-covalent interactions to develop mechanically-tunable and water-responsive composite materials via hydrogen bonding modulation. Specifically, self-assembling peptide blocks consisting of poly(β-benzyl-L-aspartate) (PBLA) are introduced into a hydrophilic polyurea system. Using these peptide–polyurea hybrids (PPUs) as a hierarchical matrix, cellulose nanocrystals (CNCs) are incorporated to diversify the self-assembled nanostructures of PPUs through matrix–filler interactions. Our findings reveal that higher PBLA content in the PPUs reduces the magnitude of the stiffness differential due to the physical crosslinking induced by the peptide blocks. Additionally, the inclusion of CNCs in the PPU matrix increases the storage modulus in the dry state but also diminishes the wet-state modulus due to the shift of physical associations from peptidic arrangements to PBLA–CNC interactions, resulting in variations in the morphology of the PPU/CNC nanocomposites. This molecular design strategy allows for the development of adaptable materials with a broad range of water-responsive storage modulus switching , spanning from ∼70 MPa to ∼400 MPa. This investigation highlights the potential of harnessing peptide assembly and peptide–cellulose interactions to achieve mechanical enhancement and water-responsiveness, providing insights for engineering next-generation responsive materials.

Multiscale modeling requires the linking of models at different levels of detail, with the goal of gaining accelerations from lower fidelity models while recovering fine details from higher resolution models. Communication across resolutions is particularly important in modeling soft matter, where tight couplings exist between molecular-level details and mesoscale structures. While multiscale modeling of biomolecules has become a critical component in exploring their structure and self-assembly, backmapping from coarse-grained to fine-grained, or atomistic, representations presents a challenge, despite recent advances through machine learning. A major hurdle, especially for strategies utilizing machine learning, is that backmappings can only approximately recover the atomistic ensemble of interest. We demonstrate conditions for which backmapped configurations may be reweighted to exactly recover the desired atomistic ensemble. By training separate decoding models for each sidechain type, we develop an algorithm based on normalizing flows and geometric algebra attention to autoregressively propose backmapped configurations for any protein sequence. Critical for reweighting with modern protein force fields, our trained models include all hydrogen atoms in the backmapping and make probabilities associated with atomistic configurations directly accessible. We also demonstrate, however, that reweighting is extremely challenging despite state-of-the-art performance on recently developed metrics and generation of configurations with low energies in atomistic protein force fields. Through detailed analysis of configurational weights, we show that machine-learned backmappings must not only generate configurations with reasonable energies, but also correctly assign relative probabilities under the generative model. These are broadly important considerations in generative modeling of atomistic molecular configurations.

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human–AI and AI–AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

The present study explores the comprehensive investigations of two methoxy-oriented chalcone structures (HK1 and HK2), each featuring distinct halogen substituents (chlorine and bromine). The crystals of the derivatives were grown and confirmed via single-crystal X-ray diffraction (XRD), revealing that HK1 crystallizes in the orthorhombic system with the space group Pbca, while HK2 crystallizes in the monoclinic system with the space group P2₁/c. Intermolecular interactions, such as hydrogen bonding, π–π stacking, and van der Waals forces, were examined for their role in molecular assembly. Hirshfeld surface analysis and enrichment ratio provided further insights into these intermolecular interactions within the lattice. Density functional theory (DFT) calculations using the B3LYP functional and 6-311++G (d,p) basis set was employed to explore the electronic structure and physicochemical properties. Quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses elucidated the topology of these compounds. In silico biological studies of the derivatives were also carried out, focusing on their inhibitory potential targeting monoamine oxidase (MAO-A and MAO-B) enzymes. Drug-likeness was evaluated through ADME-T profiling predictions, followed by molecular docking and dynamics simulations to determine the favorable binding configurations within the MAOs. Dynamics simulations over a 100 ns period confirmed the stability of the ligand–protein complexes. Overall, the present study offers a deeper understanding of the structural intricacies of the reported molecules by providing valuable insights into their chemical and biological properties through molecular interactions.

The micro-channel structure in polymers has excellent properties and is widely used in biochemistry instruments, optical sensor devices and so on. At present, numerous challenges such as low surface quality and unstable formation are faced during the fabrication of internal polymer micro-channel structures, leading to functions that do not meet expectations. In this paper, a mathematical model for channel formation in polymers is established using phase field theory, and the deformation mechanism of the microstructure driven by surface energy was studied. Next, the micro-nano-structure evolution of the polymer was simulated, and the morphology of single-channel, double-channel and Z-shaped-channel structures was studied. Finally, a comparison test of the formed structure under the action of a single temperature field and thermal-electric coupling field was carried out, and experimental results were found to be consistent with simulation results.

Size-dependent applications of biomaterials are increasing day by day, and rod-shaped biomaterials are drawing researchers attention for their different enhanced properties. Different types of chemicals are used to modify the crystal structure of hydroxyapatites (HAps); however, in this research, plant extract (Aloe vera) was chosen to control the shape of nano-crystalline HAps. This research focused on synthesizing rod-shaped hydroxyapatite using a non-toxic, environmentally friendly, low-cost, and widely available natural source. Hydrothermal technique was used to synthesize nano-hydroxyapatite (nHAp), where different volumes (0, 2.5, 5.0, and 10 mL) of plant extract were added to a water medium with raw materials [Ca(OH)₂ and H₃PO₄]. XRD, FESEM, XPS, FTIR, and optical bandgap energy calculations confirmed the formation of nHAp. Its texture coefficient and preference growth values showed that the (0 0 2) and (0 0 4) planes were the preferred growth direction when Aloe vera extract was used. Crystallite sizes were in the range of 30–72 nm, as per XRD data, and the 88–107 nm length and 31–38 nm width of rod-shaped particles was confirmed by FESEM data. Very low bandgap energies in the range of 3.56–3.81 eV were found for the synthesized nHAp. There were no significant differences in the binding energy according to XPS data, and the calculated as well as direct ratio of Ca/P and O/Ca confirmed the formation of similar nHAps.

Retraction of ‘Heteroatoms chemical tailoring of aluminum nitrite nanotubes as biosensors for 5-hydroxyindole acetic acid (a biomarker for carcinoid tumors): insights from a computational study’ by Chioma B. Ubah et al., Mol. Syst. Des. Eng., 2024, 9, 832–846, https://doi.org/10.1039/D4ME00019F.