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

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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...

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Combined Effects of Drugs: Synergism

Combined Effects of Drugs: Synergism

Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...

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...

Factors Affecting Drug Response: Overview

Factors Affecting Drug Response: Overview

When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...

Drug-Receptor Interactions

Drug-Receptor Interactions

Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....

You might also read

Related Articles

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

Sort by

Same author

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same author

AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.

BMC biology·2026

Same author

Identification and characterization of lncRNA-stemness-immune regulatory patterns.

Briefings in bioinformatics·2026

Same author

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.

Briefings in bioinformatics·2026

Same author

Intracellular bacteria modulate the immune microenvironment of oral squamous cell carcinoma.

Cell reports·2026

Same author

EnDeep4mC predicts DNA <i>N</i> <sup>4</sup>-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles.

Genome research·2026

Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026

Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026

Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026

Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026

Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026

Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph

Xue Zhang^1,2, Quan Zou^2,3, Chunyu Wang¹

¹Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Plos Computational Biology

|March 13, 2026

Summary

This summary is machine-generated.

Predicting circular RNA (circRNA) and drug sensitivity associations is crucial. A new method, CFGSCDSA, uses collaborative and graph structure learning to efficiently discover these vital links.

More Related Videos

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Related Experiment Videos

Last Updated: Mar 15, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNA (circRNA) expression correlates with drug sensitivity in human cells.
Experimental validation of circRNA-drug sensitivity links is costly and inefficient.
Efficient prediction of circRNA-drug sensitivity associations is critical.

Purpose of the Study:

To develop an efficient computational method for predicting circRNA-drug sensitivity associations.
To address data sparsity and the impact of negative samples in prediction models.

Main Methods:

A novel method, CFGSCDSA, integrates collaborative feature learning and graph structure learning.
Collaborative learning utilizes heterogeneous features from diverse data sources.
Graph structure learning incorporates a confidence-guided pseudo-labeling strategy.

Main Results:

CFGSCDSA significantly outperformed existing models in predicting circRNA-drug sensitivity associations.
Experimental evaluations confirmed the superior performance of the proposed method.
Case studies demonstrated the method's ability to identify novel associations and drug-related links.

Conclusions:

The developed CFGSCDSA method offers an efficient and accurate approach for predicting circRNA-drug sensitivity associations.
This computational strategy can accelerate the discovery of potential therapeutic targets and drug responses.
CFGSCDSA effectively overcomes limitations of traditional experimental validation methods.