Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.


Issue 24, 2018
Previous Article Next Article

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Author affiliations

Abstract

Deep learning is currently the most successful machine learning technique in a wide range of application areas and has recently been applied successfully in drug discovery research to predict potential drug targets and to screen for active molecules. However, due to (1) the lack of large-scale studies, (2) the compound series bias that is characteristic of drug discovery datasets and (3) the hyperparameter selection bias that comes with the high number of potential deep learning architectures, it remains unclear whether deep learning can indeed outperform existing computational methods in drug discovery tasks. We therefore assessed the performance of several deep learning methods on a large-scale drug discovery dataset and compared the results with those of other machine learning and target prediction methods. To avoid potential biases from hyperparameter selection or compound series, we used a nested cluster-cross-validation strategy. We found (1) that deep learning methods significantly outperform all competing methods and (2) that the predictive performance of deep learning is in many cases comparable to that of tests performed in wet labs (i.e., in vitro assays).

Graphical abstract: Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Back to tab navigation

Supplementary files

Article information


Submitted
10 Jan 2018
Accepted
16 May 2018
First published
06 Jun 2018

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2018,9, 5441-5451
Article type
Edge Article

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

A. Mayr, G. Klambauer, T. Unterthiner, M. Steijaert, J. K. Wegner, H. Ceulemans, D. Clevert and S. Hochreiter, Chem. Sci., 2018, 9, 5441
DOI: 10.1039/C8SC00148K

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

Reproduced material should be attributed as follows:

  • For reproduction of material from NJC:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
  • For reproduction of material from PCCP:
    [Original citation] - Published by the PCCP Owner Societies.
  • For reproduction of material from PPS:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
  • For reproduction of material from all other RSC journals:
    [Original citation] - Published by The Royal Society of Chemistry.

Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.


Social activity

Search articles by author

Spotlight

Advertisements