This study proposes a methodology for predicting the physical properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, features are constructed using molecular dynamics (MD) simulation results and descriptor calculation tools. This study investigates the changes in the calculated density depending on the characteristics of the training dataset and analyzes the feature characteristics across datasets to identify key features. In this study, 2,415 hydrocarbon and binder-derived polymer molecules were analyzed using MD simulations and 2,790 chemical descriptors generated using alvaDesc. The features were pre-processed using correlation filtering, PCA, and recursive feature elimination. The XGBoost models were trained using k-fold cross-validation and Optuna optimization. SHAP analysis was used to interpret feature contributions. The variables influencing the density prediction differed between the hydrocarbon and binder groups. However, the hydrogen atom count (H), van der Waals energy, and descriptors such as SpMAD_EA_LboR consistently had a strong impact. The trained models achieved high accuracy (R² > 0.99) across different datasets, and the SHAP results revealed that the edge adjacency, topological, and 3D geometrical descriptors were critical. In terms of predictive accuracy and interpretability, the integrated MDQSPR framework demonstrated high reliability for estimating the properties of individual binder polymers. This approach contributed to a molecular-level understanding and facilitated the development of ecofriendly and efficient modifiers for asphalt binders.

This study proposes a methodology for predicting properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, this study investigates the variations in molecular dynamics (MD) results with molecular structural complexity and assesses the independence and correlation of variables that influence density. In this study, MD simulations were performed on hydrocarbon-based and individual asphalt binder molecules. The effects of various temperatures, molecular conditions, and structural features on the density were analyzed. MD-related variables influencing the calculated density were evaluated and compared with experimentally measured densities. The MD-calculated densities were used as target variables in a subsequent study, in which a machine learning model was applied to perform quantitative structure–property relationship analysis.The MD-calculated densities showed a strong correlation with experimental measurements, achieving a coefficient of determination of R2 > 0.95. Potential energy exhibited a tendency to cluster into 4–6 groups depending on the molecular structure. In addition, increasing molecular weight and decreasing temperature led to higher density and viscosity. Torsional energy and other individual energy components were identified as significant factors influencing both potential energy and density. This study provided foundational data for the property prediction of asphalt binders by quantitatively analyzing the relationship between the molecular structure and properties using MD simulations. Key features that could be used in the construction of polymer structure databases and AI-based material design were also proposed. In particular, the integration of MD-based simulation and machine learning was confirmed to be a practical alternative for predicting the properties of complex polymer composite systems.

The prime objective of this computational study was to develop a highly accurate potential for the use of molecular dynamics (MD) simulations of carbon nanotubes (CNTs). This potential was generated using ab initio MD (AIMD) simulations based on density functional theory (DFT). Subsequently, we constructed machine-learned interatomic potentials (MLIPs) based on moment tensor potential (MTP) descriptors using AIMD trajectories as training data. The performance of the developed MLIPs was evaluated by conducting the MD simulations of the stress–strain responses of single-walled CNTs (SWCNTs) and defected SWCNTs (D-SWCNTs) under tensile loading. Furthermore, this work includes extensive MLIP-based MD simulations to examine the influence of diameter and chirality, temperature, and defect concentration on the fracture characteristics and Young’s modulus of SWCNTs. The findings demonstrate the computational reliability and transferability of the MLIPs in predicting the mechanical properties of SWCNTs through MD simulations performed over a temperature range of 1 K to 2000 K. The observed stiffnesses correspond to Young’s modulus ranging from 1.61–0.53 TPa with a mean value of 0.936 TPa for different SWCNTs with diameters ranging from 1.1–2.89 nm and temperatures spanning from 1 to 2000 K, exhibiting a noticeable dependence on chirality.

As a key component of composite materials, the interface quality is crucial for determining the mechanical properties of composites. Carbon fiber sizing treatment significantly enhances the fiber-matrix interface, a process extensively utilized in the carbon fiber industry. This study synthesized an environmentally friendly waterborne polyurethane sizing agent and investigated the impact of molecular weight, a critical factor, on composite performance by varying the soft segment type in the polyurethane. This research provides insights into cost-effective and eco-friendly surface treatment methods for carbon fibers and the design of robust interface structures.

Targeted protein degradation (TPD) is an emerging therapeutic strategy that leverages the natural protein degradation systems of cells to eliminate disease-associated proteins selectively. Unlike traditional small molecule inhibitors, which merely suppress protein activity, TPD degrades target proteins directly, offering a novel approach to addressing undruggable proteins. The two most extensively studied TPD technologies, proteolysis-targeting chimeras (PROTACs) and molecular glues (MGs), utilize the ubiquitin–proteasome system to induce TPD. PROTACs function as bifunctional molecules that recruit an E3 ubiquitin ligase (E3 ligase) to a target protein, leading to its ubiquitination and subsequent degradation, while MGs enhance protein–protein interactions to facilitate ubiquitination and protein clearance. These approaches have shown promising therapeutic potential in treating cancer, neurodegenerative disorders, and autoimmune diseases, with several compounds currently undergoing clinical trials. Despite these advances, challenges such as limited bioavailability, pharmacokinetic constraints, and target selectivity remain obstacles to the widespread application of TPDbased therapies. Recent developments, including the discovery of novel E3 ligases, linker optimization, and AI-driven drug design, have addressed these limitations, paving the way for the next generation of precision-targeted therapeutics. This paper provides a comprehensive overview of the mechanisms, applications, and future directions of PROTACs and MGs in drug discovery, highlighting their potential to revolutionize modern targeted therapy.