Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Colloids03:22

Colloids

20.9K
Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles that are visible to the naked eye or can be seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. On the other hand, a solution is a homogeneous mixture in which no settling occurs and in which the dissolved...
20.9K
Drug Discovery: Overview01:26

Drug Discovery: Overview

11.3K
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...
11.3K
Colloids and Suspensions01:17

Colloids and Suspensions

3.3K
Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles visible to the naked eye or seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. The suspended particles in a suspension settle out after some time of mixing. The separation of particles from a suspension is...
3.3K
Colloidal precipitates01:09

Colloidal precipitates

6.0K
The high insolubility of some precipitates can result in an unfavorable relative supersaturation. This can lead to colloidal particles with a large surface-to-mass ratio, where adsorption is promoted. For instance, in the precipitation of silver chloride, silver ions are adsorbed on the surface of the colloidal particles, forming a primary layer. This layer attracts ions of opposite charge (such as nitrate ions), forming a diffuse secondary layer of adsorbed ions. This electric double layer...
6.0K
Aggregates Classification01:29

Aggregates Classification

984
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
984
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.

ACS pharmacology & translational science·2026
Same author

Postoperative changes in cervical hemodynamics and cognitive function following cervical lymphatic-venous surgery in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2026
Same author

The Use of Deep Learning in RNA Therapeutic Development.

ACS nano·2026
Same author

Plug-and-play assembly of biodegradable ionizable lipids for potent mRNA delivery and gene editing in vivo.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Regulatory T cells sabotage anti-tumor γδ T cells by creating IL-2-deficient environments.

The Journal of experimental medicine·2026
Same author

Glutathione-Responsive Fragmentation of Heteronorbornadiene-Based Thiovinyl Sulfones in Glioma Cells.

Bioconjugate chemistry·2026

Related Experiment Video

Updated: Jan 26, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.2K

Computational advances in combating colloidal aggregation in drug discovery.

Daniel Reker1,2,3, Gonçalo J L Bernardes4,5, Tiago Rodrigues6

  • 1Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. reker@mit.edu.

Nature Chemistry
|April 17, 2019
PubMed
Summary

Colloidal aggregates in drug discovery can cause false positives, hindering lead optimization. This review highlights methods to identify and remove these problematic compounds, improving drug development efficiency.

More Related Videos

Synthesis and Characterization of Supramolecular Colloids
09:26

Synthesis and Characterization of Supramolecular Colloids

Published on: April 22, 2016

10.4K
Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment
10:13

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Published on: June 21, 2022

2.6K

Related Experiment Videos

Last Updated: Jan 26, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.2K
Synthesis and Characterization of Supramolecular Colloids
09:26

Synthesis and Characterization of Supramolecular Colloids

Published on: April 22, 2016

10.4K
Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment
10:13

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Published on: June 21, 2022

2.6K

Area of Science:

  • Medicinal Chemistry
  • Drug Discovery
  • Computational Chemistry

Background:

  • Small molecule effectors are crucial for drug discovery, requiring specific molecular recognition and reversible binding.
  • Artefactual compounds, such as frequent-hitters and assay interference agents, can negatively impact drug screening and lead optimization.
  • Colloidal aggregates are a significant source of false positive results due to protein sequestration or mimicry, yet their assessment is often overlooked.

Purpose of the Study:

  • To review the impact of colloidal aggregation on drug discovery programs.
  • To analyze examples of aggregation-related false positives from literature and public datasets.
  • To examine technologies for identifying and mitigating problematic compounds caused by aggregation.

Main Methods:

  • Literature analysis and examination of publicly available datasets.
  • Review of experimental technologies for identifying colloidal aggregates.
  • Focus on computational filters and machine learning algorithms for flagging and mitigating aggregation issues.

Main Results:

  • Colloidal aggregates are a primary cause of false positive readouts in drug discovery assays.
  • Existing methods for assessing colloidal aggregation are often under-appreciated.
  • Computational tools and machine learning offer effective strategies for identifying and eliminating problematic compounds.

Conclusions:

  • Addressing colloidal aggregation is vital for efficient and reliable drug discovery.
  • Evidence-based computational filters and machine learning can significantly mitigate the impact of aggregates.
  • Proactive identification and elimination of aggregated compounds enhance the success rate of drug development programs.