ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME a...
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Jul 8, 2024
INTRODUCTION: Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify cru...
BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, esp...
As a significant infectious disease in livestock, porcine reproductive and respiratory syndrome (PRRS) imposes substantial economic losses on the swine industry. Identification of diagnostic markers and therapeutic targets has been a focal challenge ...
Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial...
PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nu...
Journal of chemical theory and computation
Jul 8, 2024
Antibiotic resistance, particularly among Gram-negative bacteria, poses a significant healthcare challenge due to their ability to evade antibiotic action through various mechanisms. In this study, we explore the prediction of small molecule accumula...
PURPOSE: Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulat...
Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great p...
Journal of chemical information and modeling
Jul 8, 2024
In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of co...
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