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Background Understanding protein-molecular interaction is crucial for unraveling the mechanisms underlying diverse biological processes. Machine learning (ML) techniques have been extensively employed in predicting these interactions and have garnered substantial research focus. Previous studies have predominantly centered on improving model performance through novel and efficient ML approaches, often resulting in overoptimistic predictive estimates. However, these advancements frequently neglect the inherent biases stemming from network properties, particularly in biological contexts. Results In this study, we examined the biases inherent in ML models during the learning and prediction of protein-molecular interactions, particularly those arising from the scale-free property of biological networks—a characteristic where in a few nodes have many connections while most have very few. Our comprehensive analysis across diverse tasks, datasets, and ML methods provides compelling evidence of these biases. We discovered that the training and evaluation of ML models are profoundly influenced by network topology, potentially distorting model performance assessments. To mitigate this issue, we propose the degree distribution balanced (DDB) sampling strategy, a straightforward yet potent approach that alleviates biases stemming from network properties. This method further underscores the limitations of certain ML models in learning protein-molecular interactions solely from intrinsic molecular features. Conclusions Our findings present a novel perspective for assessing the performance of ML models in inferring protein-molecular interactions with greater fairness. By addressing biases introduced by network properties, the DDB sampling approach provides a more balanced and precise assessment of model capabilities. These insights hold the potential to bolster the reliability of ML models in bioinformatics, fostering a more stringent evaluation framework for predicting protein-molecular interactions.

Trichoderma spp. (Hypocreales) are used worldwide as a lucrative biocontrol agent. The interactions of Trichoderma spp. with host plants and pathogens at a molecular level are important in understanding the various mechanisms adopted by the fungus to attain a close relationship with their plant host through superior antifungal/antimicrobial activity. When working in synchrony, mycoparasitism, antibiosis, competition, and the induction of a systemic acquired resistance (SAR)-like response are considered key factors in deciding the biocontrol potential of Trichoderma . Sucrose-rich root exudates of the host plant attract Trichoderma . The soluble secretome of Trichoderma plays a significant role in attachment to and penetration and colonization of plant roots, as well as modulating the mycoparasitic and antibiosis activity of Trichoderma. This review aims to gather information on how Trichoderma interacts with host plants and its role as a biocontrol agent of soil-borne phytopathogens, and to give a comprehensive account of the diverse molecular aspects of this interaction.

With the rapid development of energy storage technology, solid‐state lithium batteries with high energy density, power density, and safety are considered as the ideal choice for the next generation of energy storage devices. Solid electrolytes have attracted considerable attention as key components of solid‐state batteries. Compared with inorganic solid electrolytes, solid polymer electrolytes have better flexibility, machinability, and more importantly, better contact with the electrode, and low interfacial impedance. However, its low ionic conductivity, narrow electrochemical stability window (ESW), and poor mechanical properties at room temperature limit its development and practical applications. In recent years, many studies have focused on improving the ionic conductivity of polymer electrolytes; however, few systematic studies and reviews have been conducted on their ESWs. A polymer electrolyte with wide electrochemical window will aid battery operation at a high voltage, which can effectively improve their energy density. Moreover, their stability toward lithium metal anode is also important. Therefore, this review summarizes the recent progress of solid polymer electrolytes on the ESW, discusses the factors affecting ESW of polymer electrolytes, and analyzes a strategy to broaden the window from the perspective of molecular interaction, polymer structural design, and interfacial tuning. The development trends of polymer electrolytes with wide electrochemical windows are also presented. Narrow electrochemical stability windows (ESW) of solid polymer electrolytes restrict their applications in high‐energy solid‐state lithium batteries. This review summarizes the factors, including intermolecular interaction, interface layer, and polymer framework, which impact their electrochemical stability, and also presents the methods to improve the ESW of solid polymer electrolytes.

Speed of sound (U), Density (ρ), and viscosity (η) values of liquid mixtures of Tetrachloroethylene with Cyclopentanone and pure liquids were determined at regular intervals of molar range at different temperatures (303.15, 308.15, 313.15, 318.15) K. From the experimentally determined values, thermo-acoustic parameters such as excess molar volume (V E ) and excess free length (L f E ), excess Gibb’s free energy (ΔG E ) and excess enthalpy (H E ) have been estimated. The variations of the above parameters discussed based on intermolecular interactions existing in this binary mixture. The computed values of sound speed in the mixture from various theories are compared with experimentally calculated values and have a clear check to validate the theories for the present system, and FTIR studies provide detailed analysis of molecular interactions.

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Beyond molecular chemistry based on the covalent bond, supramolecular chemistry aims at developing highly complex chemical systems from components interacting through noncovalent intermolecular forces. Over the past quarter century, supramolecular chemistry has grown into a major field and has fueled numerous developments at the interfaces with biology and physics. Some of the conceptual advances and future challenges are profiled here.

In order to investigate the thermal decomposition behavior of water-soluble azo dyes, a series of structurally isomeric water-soluble hydroxyl azo dyes were synthesized and their thermal decomposition phenomena were examined using TGA and DSC. Their thermal decomposition mechanism was also studied by measuring UV–Vis absorbance of the prepared dyes at various heating temperatures. From thermal analysis and spectroscopy, we found that the dyes decomposed gradually without a melting process, and those with strong inter-molecular interactions had high decomposition temperatures. Moreover, we suggested a new thermal decomposition mechanism different from that of the water-insoluble dye, in which dye molecules rapidly decomposed within a narrow temperature range following a melting process. We also found that dyes with stronger inter-molecular interactions had higher degradation temperatures using density analysis, X-ray diffraction (XRD), dynamic light scattering (DLS), and density functional theory (DFT) calculation.

The molecular interaction volume model was utilized to calculate the thermodynamic activity of the components in the following binary lead-free solder alloys: Bi-In at 900 K, Bi-Sn at 600 K, and In-Sn at 700 K. The theoretical values were compared with the corresponding experimental values. For the validation of the model parameters, the determination and comparison of the excess Gibbs free energy of mixing (GMxs) in Bi-Sn, Bi-In, and In-Sn liquid alloys were performed with the corresponding experimental data found in the literature. Significant concurrence has been noted between theoretical predictions and experimental results in binary systems. Moreover, the calculated model parameters were employed to determine the activity of indium (In) in ternary liquid alloys composed of indium, bismuth (Bi), and tin (Sn), as well as GMxs for the same ternary liquid alloys at a temperature of 813 K for all the three cross-sections, i.e., XBi:XSn = 1:2, 1:1, and 2:1. The arrangement between the predicted and experimental outcomes is generally satisfactory for the ternary alloys, except for the case of XBi:XSn = 2:1.

The maximum temperature limit at which liquid boils explosively is referred to as the superheat limit of liquid. Through various experimental studies on the superheating limit of liquids, rapid evaporation of liquids has been observed at the superheating limit. This study explored the water nucleation process at the superheat limit achieved in micro-platinum wires using a molecular interaction model. According to the molecular interaction model, the nucleation rate and time delay at 576.2 K are approximately 2.1 × 1011/(μm3μs) and 5.7 ns, respectively. With an evaporation rate (116.0 m/s) much faster than that of hydrocarbons (14.0 m/s), these readings show that explosive boiling or rapid phase transition from liquid to vapor can occur at the superheat limit of water. Subsequent bubble growth after bubble nucleation was also considered.

Investigating molecular interactions is crucial for advancing biological research and therapeutic discovery. Traditional analytical techniques often face limitations in sensitivity, quantification accuracy, and simplicity. Metasurfaces support resonances that are widely explored both for far-field wavefront shaping and for near-field sensing. Here, we introduce an innovative inverse-designed multilayer metasurface plasmon resonance (IDMM-SPR) sensor that overcomes these challenges. Using a double-objective optimization method that combines numerical simulations and machine learning, we developed an IDMM-SPR sensor featuring an optimized periodic nanocup array. This design yields unparalleled sensitivity and stability by harnessing collective resonances—including localized SPR, Wood’s anomalies, and the Bloch wave SPR—which collectively enhance the sensing performance to enable the analysis of ultra-high affinity and low molecular weight interactions. The sensor achieves a figure of merit (FoM) of 26.3 and a detection limit (LOD) for C-reactive protein (CRP) as low as 0.84 ng/mL. Its compatibility with microplate absorbance readers and imaging detection makes it highly practical. The IDMM-SPR sensor shows exceptional promise for high-throughput direct molecular fishing, drug development, and disease diagnosis, offering a powerful tool for real-time, label-free affinity detection and quantitative analysis of biomolecular interactions. Graphical Abstract Inverse-Designed Plasmonic Biosensors with LSPR-SPP-Wood Anomaly Coupling Enhanced for Biomolecular Analysis.