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Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti‐schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti‐schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer‐aided methods. To address this issue, we proposed GNN‐DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti‐schistosome (DDAS) small molecules. Initially, a multi‐layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti‐schistosome small molecules. Experimental results showed that GNN‐DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real‐world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN‐DDAS. Overall, GNN‐DDAS provided a promising solution for discovering new and active anti‐schistosome small molecules. [ABSTRACT FROM AUTHOR]

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96–98% even when different enzyme–substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design. [ABSTRACT FROM AUTHOR]

A new pregnane steroid, named aspergillon A (1), together with two known compounds, (22E,24R)-ergosta-5,7,22-trien-3β-ol (2) and (22E, 24R)-ergosta-4,6,8(14),22-tetraen-3-one (3) were isolated from cultures of the tin mine tailings-associated fungus Aspergillus versicolor. The new structure and absolute configuration were determined with the help of extensive spectroscopic analyses and quantum chemical calculations of the electronic circular dichroism (ECD) spectra. [ABSTRACT FROM AUTHOR]

A new cyclohexenone, named phomaligol D (1), together with two known compounds, kojic acid (2) and phomaligol A (3) were isolated from the tin mine tailings-derived fungus Aspergillus flavus YIM DT 10012. Their structures were elucidated by detailed analysis of spectroscopic data. [ABSTRACT FROM AUTHOR]

Stachartone A (1), a novel phenylspirodrimane dimer, was isolated from cultures of the tin mine tailings-associated fungus Stachybotrys chartarum. Its structure was elucidated by means of spectroscopic methods. At the same time, compound (1) was tested for its cytotoxicity against five human cancer cell lines. [ABSTRACT FROM AUTHOR]

One new quinolinone, 7-hydroxy-3-methoxyviridicatin (1), along with eight known compounds (2–9) was isolated from the fungus Myrothecium verrucaria, which was collected from lake water of Chenghai, Yunnan Province, China. Their structures were elucidated by detailed analysis of spectroscopic data and comparison with related known compounds. Compounds 1 and 2 exhibited weak antibacterial activity. To the best of our knowledge, this is the first report on quinolinones (1–4) as the secondary metabolites of M. verrucaria. [ABSTRACT FROM AUTHOR]