Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Artificial intelligence-based toxicity prediction of environmental chemicals: future directions for chemical management applications
Recently, research on the development of artificial intelligence (AI)-based computational
toxicology models that predict toxicity without the use of animal testing has emerged …
toxicology models that predict toxicity without the use of animal testing has emerged …
Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
Opportunities and challenges in application of artificial intelligence in pharmacology
Artificial intelligence (AI) is a machine science that can mimic human behaviour like
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
Machine learning in predictive toxicology: recent applications and future directions for classification models
MWH Wang, JM Goodman… - Chemical research in …, 2020 - ACS Publications
In recent times, machine learning has become increasingly prominent in predictive
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
X Wu, Q Zhou, L Mu, X Hu - Journal of Hazardous Materials, 2022 - Elsevier
Over the past few decades, data-driven machine learning (ML) has distinguished itself from
hypothesis-driven studies and has recently received much attention in environmental …
hypothesis-driven studies and has recently received much attention in environmental …
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 …
An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors
Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
[HTML][HTML] QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia …
GJ Lavado, D Baderna, E Carnesecchi… - Journal of Hazardous …, 2022 - Elsevier
Soil pollution is a critical environmental challenge: the substances released in the soil can
adversely affect humans and the ecosystem. Several bioassays were developed to …
adversely affect humans and the ecosystem. Several bioassays were developed to …
Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints
Reproductive toxicity endpoints are a significant safety concern in the assessment of the
adverse effects of chemicals in drug discovery. Computational models that can accurately …
adverse effects of chemicals in drug discovery. Computational models that can accurately …