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Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol–water log P blind challenge

  • Shuzhe Wang
  • Sereina RinikerEmail author
Article

Abstract

The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol–water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol–water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package.

Keywords

Molecular dynamics Machine learning SAMPL6 

Notes

Acknowledgements

The authors gratefully acknowledge financial support by the Swiss National Science Foundation (Grant Number 200021-178762) and by ETH Zurich (ETH-34 17-2). They further acknowledge SAMPL NIH Grant 1R01GM124270-01A1 for support of the experimental work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10822_2019_252_MOESM1_ESM.txt (1.5 mb)
Electronic supplementary material 1 (TXT 1576 kb)
10822_2019_252_MOESM2_ESM.txt (349 kb)
Electronic supplementary material 2 (TXT 349 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory of Physical ChemistryETH ZurichZurichSwitzerland

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