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Related Experiment Videos

Badapple: promiscuity patterns from noisy evidence.

Jeremy J Yang¹, Oleg Ursu¹, Christopher A Lipinski²

¹Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA.

Journal of Cheminformatics

|May 31, 2016

Summary

This summary is machine-generated.

Identifying promiscuous compounds early in drug discovery is crucial. Badapple (bioassay-data associative promiscuity pattern learning engine) is a new algorithm that uses scaffold associations to rapidly flag compounds likely to cause false trails, saving resources.

Keywords:

Compound promiscuity Drug discovery informatics High-throughput screening (HTS)Molecular scaffolds Statistical learning

Related Experiment Videos

Area of Science:

Drug discovery and chemical biology
Bioassay data analysis
Computational chemistry

Background:

Bioassay data analysis is essential but challenging in drug discovery.
Inferring reliable knowledge from large, noisy datasets requires robust methods.
Early identification of promiscuous compounds avoids wasting resources on

Purpose of the Study:

To develop an automated algorithm for identifying likely promiscuous compounds.
To assist and accelerate drug discovery informatics.
To streamline hit selection by avoiding

Main Methods:

Developed Badapple (bioassay-data associative promiscuity pattern learning engine).
Combined general data science and domain-specific bioassay features.
Analyzed data from MLP assays using the BioAssay Research Database (BARD).

Main Results:

Badapple algorithm identifies likely promiscuous compounds via associated scaffolds.
Analysis of MLP assay data from BARD demonstrated its utility.
Specific examples illustrate associations with known promiscuity mechanisms.

Conclusions:

Badapple rapidly identifies likely promiscuous compounds using scaffold associations.
The method is evidence-driven, automated, and self-improving.
It provides a score for empirical promiscuity, streamlining workflows and avoiding false trails.