Related Experiment Video
Updated: Aug 28, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation
Matthew J Sniatynski1,2, Bruce S Kristal1,2
1Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA.
Abstract:
Hao et al. (2022) present DTox (deep learning for toxicology), a neural network designed to predict and probe the sites and potential mechanisms underlying chemical toxicity; results provide a map to facilitate modular testing and improvements across multiple disparate applications.
Related Concept Videos
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Drug Discovery: Overview
Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
Nonlinear Pharmacokinetics: Overview
Nonlinearity can arise due to the saturation of plasma protein-binding or...
Analysis of Population Pharmacokinetic Data
Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

