Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action

Maris Lapins, View ORCID ProfileOla Spjuth
doi: https://doi.org/10.1101/580654
Maris Lapins
1Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: maris.lapins@farmbio.uu.se
Ola Spjuth
1Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ola Spjuth
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Profiling drug leads by means of in silico and in vitro assays as well as omics is widely used in drug discovery for safety and efficacy predictions. In this study, we evaluate the performance of machine learning models trained on data from gene expression and phenotypic profiling assays, with models trained on chemical structure descriptors, for prediction of various drug mechanisms of action and target proteins. Models for several hundred mechanisms of actions and targets were trained using data on 1484 compounds characterized in both gene expression using L1000 profiles, and phenotypic profiling with cell painting assay. The results indicate that the accuracy of the three profiling technologies varies for different endpoints, and indicate a clear potential synergistic effect if these methods are combined. We also study the effect of predictive accuracy of data from different cell lines for L1000 profiles, showing that the choice of cell line has a non-negligible effect on the predictive accuracy. The results strengthen the idea of integrated approaches for predicting drug targets and mechanisms of action in preclinical drug discovery.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 03, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action
Maris Lapins, Ola Spjuth
bioRxiv 580654; doi: https://doi.org/10.1101/580654
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action
Maris Lapins, Ola Spjuth
bioRxiv 580654; doi: https://doi.org/10.1101/580654

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9152)
  • Bioengineering (6789)
  • Bioinformatics (24037)
  • Biophysics (12142)
  • Cancer Biology (9550)
  • Cell Biology (13808)
  • Clinical Trials (138)
  • Developmental Biology (7649)
  • Ecology (11719)
  • Epidemiology (2066)
  • Evolutionary Biology (15522)
  • Genetics (10654)
  • Genomics (14337)
  • Immunology (9495)
  • Microbiology (22872)
  • Molecular Biology (9113)
  • Neuroscience (49070)
  • Paleontology (355)
  • Pathology (1485)
  • Pharmacology and Toxicology (2572)
  • Physiology (3851)
  • Plant Biology (8341)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2299)
  • Systems Biology (6199)
  • Zoology (1302)