Artificial intelligence for drug toxicity and safety
Interventional pharmacology is one of medicine's most potent weapons against disease.
These drugs, however, can result in damaging side effects and must be closely monitored …
These drugs, however, can result in damaging side effects and must be closely monitored …
Review of machine learning and deep learning models for toxicity prediction
The ever-increasing number of chemicals has raised public concerns due to their adverse
effects on human health and the environment. To protect public health and the environment …
effects on human health and the environment. To protect public health and the environment …
Machine learning models for predicting cytotoxicity of nanomaterials
The wide application of nanomaterials in consumer and medical products has raised
concerns about their potential adverse effects on human health. Thus, more and more …
concerns about their potential adverse effects on human health. Thus, more and more …
Deep learning models for predicting gas adsorption capacity of nanomaterials
Metal–organic frameworks (MOFs), a class of porous nanomaterials, have been widely used
in gas adsorption-based applications due to their high porosities and chemical tunability. To …
in gas adsorption-based applications due to their high porosities and chemical tunability. To …
Machine learning and deep learning for brain tumor MRI image segmentation
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
Development of decision forest models for prediction of drug-induced liver injury in humans using a large set of FDA-approved drugs
Drug-induced liver injury (DILI) presents a significant challenge to drug development and
regulatory science. The FDA's Liver Toxicity Knowledge Base (LTKB) evaluated> 1000 …
regulatory science. The FDA's Liver Toxicity Knowledge Base (LTKB) evaluated> 1000 …
Molecular dynamics simulations and applications in computational toxicology and nanotoxicology
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical
researches to explore toxicity of various biological systems. Investigating biological systems …
researches to explore toxicity of various biological systems. Investigating biological systems …
High-throughput screening to identify chemical cardiotoxic potential
S Krishna, B Berridge… - Chemical Research in …, 2020 - ACS Publications
Cardiovascular (CV) disease is one of the most prevalent public health concerns, and
mounting evidence supports the contribution of environmental chemicals to CV disease …
mounting evidence supports the contribution of environmental chemicals to CV disease …
Experimental data extraction and in silico prediction of the estrogenic activity of renewable replacements for bisphenol A
Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide
array of applications; however, increasing evidence has shown that BPA causes significant …
array of applications; however, increasing evidence has shown that BPA causes significant …
Machine learning models for predicting liver toxicity
Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials
and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage …
and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage …