Knowledge, attitude, and practice of orthodontic treatment among student patients preparing for or undergoing treatment.

This study aimed to investigate the levels of knowledge, attitude and practice (KAP) of orthodontic treatment among student patients either preparing for or actively undergoing orthodontic treatment. This cross-sectional study was conducted at a tertiary dental hospital in southwest China between August, 2023 and February, 2024. Demographic characteristics and KAP scores were collected using a self- reported questionnaire.

A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substrate pairs turnover number data. However, the performance of existing approaches still heavily depends on the effectiveness of feature extraction for enzymes and substrates, as well as the optimal fusion of these two types of features.

MTGGF: A Metabolism Type-Aware Graph Generative Model for Molecular Metabolite Prediction.

Metabolism in vivo turns small molecules (e.g., drugs) into metabolites (new molecules), which brings unexpected safety issues in drug development.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection.

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

Motivation: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice.

The Function of Spag6 to Repair Brain Edema Damage After Cerebral Ischemic Stroke-reperfusion.

Sperm associated antigen 6 (Spag6) is the PF16 homolog of Chlamydomonas and participates in the regulation of cilia movement. Studies have shown that Spag6 is expressed in the brain, and its loss will lead to cerebral edema caused by a defect in motor cilium function in ependymal cells. However, it has not been reported whether the limited or extensive cerebral edema resulting from ischemic strokes is related to the expression regulation of Spag6.

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

Motivation: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other.

Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with the advantage of low cost and high efficiency. This review provides a comprehensive survey of DL-based CPI prediction.