Current clinical treatment of pulmonary diseases requires an advanced three-dimensional (3D) pathological atlas of the microenvironment, particularly the trachea, which is predominantly affected by lung disorders. In this study, the gridded cyclodext...
This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorica...
Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. ...
Journal of controlled release : official journal of the Controlled Release Society
Aug 16, 2024
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tum...
Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs is extremely difficult, and the distribution, translocation, and metab...
Journal of chemical information and modeling
Apr 19, 2024
Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel q...
A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for a...
BACKGROUND: Radioembolization with Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.
Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combini...
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...
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