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 Experiment Videos

Selection criteria for drug-like compounds.

Ingo Muegge1

  • 1Bayer Research Center, 400 Morgan Lane, West Haven, Connecticut 06516, USA. imugge@rdg.boehringer-ingelheim.com

Medicinal Research Reviews
|March 21, 2003
PubMed
Summary

Identifying quality drug leads is harder due to increased compound libraries. This review focuses on computational methods for assessing

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

How does machine learning augment alchemical binding free energy calculations?

Future medicinal chemistry·2025
Same author

Perspectives on current approaches to virtual screening in drug discovery.

Expert opinion on drug discovery·2024
Same author

Advances in virtual screening.

Drug discovery today. Technologies·2024
Same author

Prediction of Human Clearance Using In Silico Models with Reduced Bias.

Molecular pharmaceutics·2024
Same author

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery.

ACS medicinal chemistry letters·2023
Same author

How do we further enhance 2D fingerprint similarity searching for novel drug discovery?

Expert opinion on drug discovery·2022

Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery

Background:

  • High-throughput screening (HTS) faces challenges in identifying novel drug leads.
  • Large random compound libraries yield diminishing returns for drug discovery.
  • Low success rates in converting leads to drugs highlight pharmacokinetic issues.

Purpose of the Study:

  • To review computational techniques for evaluating compound drug-likeness.
  • To provide an outlook on advancing drug-likeness assessment in drug discovery.

Main Methods:

  • Retrospective analysis of known drug collections.
  • Development of algorithms to capture 'chemical wisdom'.
  • Application of machine learning, regression, and clustering methods.

Main Results:

  • Various computational approaches exist to distinguish drugs from non-drugs.
  • Drug-likeness assessment is crucial for improving drug discovery success rates.

Conclusions:

  • Computational methods are essential for optimizing compound selection in drug discovery.
  • Further development in computational drug-likeness assessment is needed for future pharmaceutical innovation.

Related Experiment Videos