Learn about cutting-edge methods and models for predicting chemical toxicity in this 36-minute lecture by Professor Alexander Tropsha, an expert in computational chemistry and cheminformatics. Explore the proliferation of non-animal testing methods, computational and in vitro toxicology trends, and major approaches to toxicity prediction. Examine structural alerts and QSAR models, focusing on examples like cardiotoxicity prediction. Discover integrative approaches combining structural alerts and QSAR models for chemical safety assessment. Delve into advanced techniques such as the Descriptor Integration approach and Chemical-biological read-across (CBRA). Understand the importance of rigorous validation in QSAR modeling workflows and learn about computational models for predicting outcomes of acute toxicity tests. Gain valuable insights from Professor Tropsha's extensive experience in biomolecular informatics and computer-assisted drug design.
Methods and Models for Chemical Toxicity Prediction - 2022
School of Chemoinformatics in Latin America via YouTube
Overview
Syllabus
Intro
Proliferation of non-animal methods (NAMs) for toxicity testing
Computational and in vitro toxicology market trends
Major classes of approaches to toxicity prediction: Structural alerts vs. QSAR
Alerts vs. QSAR: examination of tertiary amine or arylchoride cardiotoxicity (hERG blockage) alerts
Chemical Alerts of Toxicity: what are they for, really?
Integrative approaches for chemical safety assessmen of new chemicals by combining structural alerts and QSAR models
Descriptor Integration approach (CBRA) Predicting Subchronic Hepatotoxicity from 24h Toxicogenomics Profiles
Conflicting Predictions by QSAR and Toxicogenomics Models
Chemical-biological read-across (CBRA): learning from both sets of neighbors
QSAR Modeling Workflow: the importance of rigorous validation
Computational models to predict the outcomes of the "six-pack" battery of acute toxicity tests
Taught by
School of Chemoinformatics in Latin America
