Globally, industries and regulatory authorities are faced with an urgent need to assess the
potential adverse effects of chemicals more efficiently by embracing new approach …

Adult neurotoxicity (ANT) and developmental neurotoxicity (DNT) assessments aim to
understand the adverse effects and underlying mechanisms of toxicants on the human …

Neurotoxicology is the study of adverse effects on the structure or function of the develo**
or mature adult nervous system following exposure to chemical, biological, or physical …

We present a mechanistic machine-learning quantitative structure-activity relationship
(QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat …

The field of toxicology and safety assessment is in a major transformation period
represented by a shift from animal-intensive observational science to new approach …

Aim: This study reports the designing of BChE inhibitors through machine learning (ML),
followed by in silico and in vitro evaluations. Methodology: ML technique was used to predict …

Many companies and global regulatory programs have expressed the intent to move away
from in vivo animal testing to new approach methods (NAMs) as part of product safety …

Introduction: We are develo** computational models for basic nervous system pathways
for use in toxicology, pharmacology, and medicine. γ-Amino butyric acid (GABA) is the major …

The chapter describes in silico methodologies and models based on structure-activity
relationships to predict toxicological endpoints. It provides an overview of relevant in silico …

This chapter briefly explores the principles and application of predictive computational
approaches (cheminformatics) to the field of toxicology. In recent years, regulatory and …