AIMC Topic: Toxicology
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Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach.
Journal of applied toxicology : JAT
Nov 2, 2020
Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astroc...
Role of Artificial Intelligence and Machine Learning in Nanosafety.
Small (Weinheim an der Bergstrasse, Germany)
Jun 15, 2020
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. N...
Revealing cytotoxic substructures in molecules using deep learning.
Journal of computer-aided molecular design
Apr 16, 2020
In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical a...
The Role and Promise of Artificial Intelligence in Medical Toxicology.
Journal of medical toxicology : official journal of the American College of Medical Toxicology
Mar 25, 2020
Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias...
Artificial intelligence and big data facilitated targeted drug discovery.
Stroke and vascular neurology
Nov 7, 2019
Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database in...
Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology.
Toxicologic pathology
Oct 23, 2019
Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) a...
Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.
Chemical research in toxicology
May 10, 2018
Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of ti...
Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.
Toxicology and applied pharmacology
Mar 13, 2018
Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mech...
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.
Scientific reports
Dec 11, 2017
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 drugs for their likelihood of causing DILI in humans, of which >700 drugs wer...
The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.
Expert review of gastroenterology & hepatology
Oct 9, 2017
Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can b...
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