Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Quantitative Aspects of Drug-Receptor Interaction

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Genomics-Driven Immunotherapy: Advancing Cancer Treatment through Personalized Approaches.

Current genomics·2026

Same author

Development of <i>Phyllanthus emblica</i> Extract-Loaded Niosomes for Cancer Treatment: Formulation and In Vitro Evaluation.

Pharmaceuticals (Basel, Switzerland)·2026

Same author

Graph-Based Classification with GNN-Explainer for Predicting Cardiac Toxicity Associated with Multi-Ion Channel Blockers.

Chemical research in toxicology·2026

Same author

Computational toxicology of <i>N</i>-nitrosamine impurities: from molecular structure to regulatory concern.

Toxicology mechanisms and methods·2026

Same author

Discovery of putative G-protein-biased µ-opioid agonists via hierarchical virtual screening of ultra-large chemical space.

Molecular diversity·2026

Same author

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands.

ACS medicinal chemistry letters·2025

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Developing a predictive QSAR model for FGFR-1 inhibitors: integrating computational and experimental validation.

Sandip D Nagare¹, Sharav A Desai², Vipul P Patel¹

¹Department of Pharmaceutical Biotechnology, Sanjivani College of Pharmaceutical Education and Research, Savitribai Phule Pune University, Kopargaon, Maharashtra, 423603, India.

Journal of Computer-Aided Molecular Design

|October 4, 2025

Summary

This study developed a computational model to predict Fibroblast Growth Factor Receptor 1 (FGFR-1) inhibitors for cancer therapy. Oleic acid showed promising inhibitory effects with low toxicity, enhancing drug discovery efficiency.

Keywords:

Anticancer drugs Drug design FGFR-1 In vitro validation Machine learning QSAR

More Related Videos

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Related Experiment Videos

Last Updated: Jun 29, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Area of Science:

Computational Chemistry
Drug Discovery
Oncology

Background:

Traditional drug discovery is inefficient, costly, and has high failure rates.
Fibroblast Growth Factor Receptor 1 (FGFR-1) is implicated in various cancers, necessitating targeted inhibitors.
Innovative strategies are crucial to optimize therapeutic candidate identification.

Purpose of the Study:

To develop a quantitative structure-activity relationship (QSAR) model for predicting FGFR-1 inhibitory activity.
To identify novel FGFR-1 inhibitors using computational and experimental approaches.
To enhance the efficiency and accuracy of the drug discovery process for cancer therapeutics.

Main Methods:

Developed a QSAR model using multiple linear regression (MLR) on 1779 compounds from the ChEMBL database.
Employed feature selection, 10-fold cross-validation, and external validation for model assessment.
Validated predictions using molecular docking, molecular dynamics simulations, and in vitro assays (MTT, wound healing, clonogenic) on cancer and normal cell lines.

Main Results:

The QSAR model demonstrated strong predictive performance (R²train=0.7869, R²test=0.7413).
Molecular simulations confirmed stable interactions between compounds and FGFR-1.
In vitro assays correlated predicted and observed activities; oleic acid showed significant inhibition of A549 and MCF-7 cells with low cytotoxicity.

Conclusions:

The integrated computational and experimental approach significantly improved drug discovery efficiency for FGFR-1 inhibitors.
The developed QSAR model accurately predicts compound activity against FGFR-1.
Oleic acid emerges as a promising lead compound for lung and breast cancer treatment with a favorable safety profile.