QSAR (quantitative structure activity relationship) - Toxicology

What is QSAR?

Quantitative Structure-Activity Relationship (QSAR) is a computational method used to predict the activity or toxicity of chemical compounds based on their chemical structure. It correlates the physical and chemical properties of compounds with their biological activities.

How Does QSAR Work?

QSAR models are built using statistical techniques to analyze the relationship between the molecular descriptors of compounds and their observed biological activities. These descriptors can include properties such as hydrophobicity, electronic properties, and molecular geometry. The resulting model can then be used to predict the activity or toxicity of new, untested compounds.

Why is QSAR Important in Toxicology?

In toxicology, QSAR is crucial because it provides a way to estimate the potential toxicity of new chemicals without the need for extensive in vitro or in vivo testing. This is particularly important for high-throughput screening of large chemical libraries and for regulatory purposes, where the safety of chemicals must be assessed.

What are the Applications of QSAR in Toxicology?

QSAR models are widely used in various applications within toxicology, including:

Drug development: Predicting the toxicity of new drug candidates.
Environmental risk assessment: Estimating the toxicity of pollutants and industrial chemicals.
Regulatory toxicology: Assisting in the safety evaluation of chemicals for regulatory approval.
Cosmetics: Ensuring the safety of ingredients in cosmetic products.

What are the Limitations of QSAR?

Despite its advantages, QSAR has several limitations:

Data quality: The accuracy of QSAR models depends on the quality and quantity of the experimental data used to build them.
Applicability domain: QSAR models are most reliable within the chemical space they were trained on and may not perform well for compounds outside this domain.
Complexity of biological systems: Biological interactions are complex and may not be fully captured by the molecular descriptors used in QSAR models.

What is the Future of QSAR in Toxicology?

The future of QSAR in toxicology looks promising, with ongoing advancements in machine learning and artificial intelligence enhancing the predictive power of these models. Additionally, the integration of omics data (genomics, proteomics, metabolomics) and big data analytics is expected to further improve the accuracy and applicability of QSAR models in predicting toxicity.

Relevant Publications

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

Issue Release: 2025

Molecular Similarity in Predictive Toxicology with a Focus on the q-RASAR Technique.

Issue Release: 2025

Applicability Domain for Trustable Predictions.

Issue Release: 2025

The Database Makes the Poison: How the Selection of Datasets in QSAR Models Impacts Toxicant Prediction of Higher Tier Endpoints.

Issue Release: 2024

Synthesis, biological evaluation, theoretical calculations, QSAR and molecular docking studies of novel arylaminonaphthols as potent antioxidants and BChE inhibitors.

Issue Release: 2024

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions.

Issue Release: 2024

Amphiphilic amidines as potential plasmic membrane-targeting antifungal agents: synthesis, bio-activities and QSAR.

Issue Release: 2024

An in Silico Study on Human Carcinogenicity Mechanism of Polybrominated Biphenyls Exposure.

Issue Release: 2024

Quantitative Read-Across Structure-Activity Relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats.

Issue Release: 2024

Synthesis, kinetic studies, and QSAR of dinucleoside polyphosphate derivatives as human AK1 inhibitors.

Issue Release: 2024

Potent inhibition of human and rat 17β-hydroxysteroid dehydrogenase 1 by curcuminoids and the metabolites: 3D QSAR and in silico docking analysis.

Issue Release: 2024

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity.

Issue Release: 2024

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Issue Release: 2024

Computer-Aided Design and Biological Evaluation of Diazaspirocyclic DR Antagonists.

Issue Release: 2024

Synthesis of Acrylopimaric Acid Triazole Derivatives and Their Antioomycete Activity against .

Issue Release: 2024

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

Issue Release: 2024

The substitution sites of hydroxyl and galloyl groups determine the inhibitory activity of human pancreatic α-amylase in twelve tea polyphenol monomers.

Issue Release: 2024

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Issue Release: 2024

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model.

Issue Release: 2024

Structure-activity Relationship Studies on VEGFR2 Tyrosine Kinase Inhibitors for Identification of Potential Natural Anticancer Compounds.

Issue Release: 2024

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