Related Experiment Video
Updated: Jul 6, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
The importance of the domain of applicability in QSAR modeling
1Computational & Structural Chemistry, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom.
Abstract:
The domain of applicability is an important concept in quantitative structure activity relationships (QSAR) that allows one to estimate the uncertainty in the prediction of a particular molecule based on how similar it is to the compounds used to build the model. In this paper we discuss this important concept, providing details of the development and application of a method to compute the domain of applicability within model descriptor space and structural space as defined by daylight fingerprints. The importance of the domain of applicability is illustrated using five QSAR models generated on plasma protein binding and P450 inhibition datasets. Such methodologies will be shown to offer us a means to monitor the performance of QSARs over time, providing us both with a way to estimate the accuracy of a given prediction and identify when a model needs to be rebuilt.
More Related Videos
10:29Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
05:50Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
Published on: September 26, 2025
Related Concept Videos
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 its...
Quantitative Aspects of Drug-Receptor Interaction
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.
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...