Aristolochic acids are known for nephrotoxicity, and implicated in multiple cancer types such as hepatocellular carcinomas demonstrated by recent studies. Natural products that are analogues to aristolochic acids have been constantly isolated from organisms; a larger chemical space of these compounds and a wider coverage of biological sources should be determined in consideration of the potential hazard of aristolochic acid analogues and the wide distribution of their biological sources in the nature. Therefore, we carried out an in silico research of naturally occurring aristolochic acid analogues and their biological sources, as a supplement to existing studies. The result shows a chemical space of 238 naturally occurring aristolochic acid analogues that are present in 175 species of biological sources including 44 traditional medicines. With the computational estimation for toxicity and the implication in hazard assessment of a biological source with the presence of aristolochic acid analogues, we propose that additional awareness should be raised to the public for avoidance of toxic species, especially those that are used as herbal medicines and easily accessible.

Chemically unstable natural products are prone to show their reactivity in the procedures of extraction, purification, or identification and turn into contaminants as so-called “artifacts”. However, identification of artifacts requires considerable investments in technical equipment, time, and human resources. For revealing these reactive natural products and their artifacts by computational approaches, we set up a virtual screening system to seek cases in a biochemical database. The screening system is based on deep learning models of predicting the two main classifications of conversion reactions from natural products to artifacts, namely solvolysis and oxidation. A set of result data was reviewed for checking validity of the screening system, and we screened out a batch of reactive natural products and their probable artifacts. This work provides some insights into the formations of natural product artifacts, and the result data may act as warnings regarding the improper handling of biological matrixes in multicomponent extraction.

Two models for predicting the density of organic cocrystals composed of energetic organic cocrystals and general organic cocrystals containing nitro groups were obtained. Sixty organic cocrystals in which the ratio of component molecules is 1 : 1 were studied as the dataset. Model-I was based on the artificial neural network (ANN) to predict the density of the cocrystals, which used (six) input parameters of the component molecules. The root mean square error (RMSE) of the ANN model was 0.033, the mean absolute error (MAE) was 0.023, and the coefficient of determination (R²) was 0.920. Model-II used the surface electrostatic potential correction method to predict the cocrystal density. The corresponding RMSE, MAE, and R² were 0.055, 0.045, and 0.716, respectively. The performance of Model-I is better than that of Model-II.

The matrix and particle interface of hydroxyl-terminated polybutadiene (HTPB) propellant is the weakest part of its mechanical properties, making it prone to dewetting damage and destroying the structural integrity of the propellant. This article uses nano-indentation, gas cluster ion beam etching, and X-ray photoelectron spectroscopy experimental analyses to study the physicochemical properties of the interface and subsequently construct a microscopic model for the HTPB matrix and ammonium perchlorate particle interface. The model fully considers the existing forms of curing agent toluene diisocyanate, bonding agent Tris (2-methyl-aziridine) phosphine oxide (MAPO), and the aging products of the propellant in the interface structure. Meanwhile, the physicochemical properties, mechanical properties, and adhesive properties of the interface under different tensile loading conditions were analyzed. The results indicate that the bonding agent MAPO significantly enhances the mechanical and adhesive properties of the interface. The interface is sensitive to changes in temperature and tensile rate, and the aged interface is more fragile.