Diversified crop rotations exert a profound influence on the nosZI denitrifier community.

Agricultural soils are a significant contributor to global nitrous oxide (NO) emissions, which is primarily driven by microbial nitrification and denitrification processes. Diversifying crop rotations can enhance soil nitrogen (N) utilization and influence N-cycling microbes, particularly the denitrifiers. Here, we evaluated the abundance, diversity, and community structure of soil denitrifiers by analyzing the denitrification genes (nirS, nirK, and nosZI) with a 14-year experiment of continuous and rotated crop systems.

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design.

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures.

Reward recalibrates rule representations in human amygdala and hippocampus intracranial recordings.

Adaptive behavior requires the ability to shift responding within (intra-dimensional) or between (extra-dimensional) stimulus dimensions when reward contingencies change. Studies of shifting in humans have focused mainly on the prefrontal cortex and/ or have been restricted to indirect measures of neural activity such as fMRI and lesions. Here, we demonstrate the importance of the amygdala and hippocampus by recording local field potentials directly from these regions intracranially in human epilepsy patients.

Improved Ensemble Model for Insecticide Recognition by Incorporating Insect Toxicity Data.

Pesticide molecules, such as insecticides, play a critical role in modern agricultural production. Traditional pesticide development methods are often inefficient and expensive, while data-driven artificial intelligence (AI) techniques have emerged as a useful tool to facilitate drug discovery. However, currently available commercial pesticide data is limited, which makes the trained models unsatisfactory in terms of performance and generalization.

AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2.

Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands.

Identification of potential extracellular signal-regulated protein kinase 2 inhibitors based on multiple virtual screening strategies.

The integration of multiple virtual screening strategies facilitates the balance of computational efficiency and prediction accuracy. In this study, we constructed an efficient and reliable "multi-stage virtual screening-in vitro biological validation" system to identify potential inhibitors targeting extracellular signal-regulated protein kinase 2 (ERK2). Firstly, we rapidly obtained 10 candidate ERK2 inhibitors with desirable pharmacokinetic characteristics from thousands of named natural products in ZINC database based on machine learning classification models and ADME/T prediction.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance.

Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots.

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation.

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity.

Multi-stage virtual screening of natural products against p38α mitogen-activated protein kinase: predictive modeling by machine learning, docking study and molecular dynamics simulation.

p38α is a mitogen-activated protein kinase (MAPK), and the signaling pathways involved are closely related to the inflammation, apoptosis and differentiation of cells, which also makes it an attractive target for drug discovery. With the high efficiency and low cost, virtual screening technology is becoming an indispensable part of drug development. In this study, a novel multi-stage virtual screening method based on machine learning, molecular docking and molecular dynamics simulation was developed to identify p38α MAPK inhibitors from natural products in ZINC database, which improves the prediction accuracy by considering and utilizing both ligand and receptor information compared to any individual approach.

Identification of potential matrix metalloproteinase-2 inhibitors from natural products through advanced machine learning-based cheminformatics approaches.

Matrix metalloproteinase-2 (MMP-2) is capable of degrading Collage TypeIV in the vascular basement membrane and extracellular matrix. Studies have shown that MMP-2 is tightly associated with the biological behavior of malignant tumors. Therefore, the identification of inhibitors targeting MMP-2 could be effective in treating the disease by maintaining extracellular matrix homeostasis.