Literature DB >> 34125358

COSMO-RS predictions of logP in the SAMPL7 blind challenge.

Judith Warnau1, Karin Wichmann2, Jens Reinisch2.   

Abstract

We applied the COSMO-RS method to predict the partition coefficient logP between water and 1-octanol for 22 small drug like molecules within the framework of the SAMPL7 blind challenge. We carefully collected a set of thermodynamically meaningful microstates, including tautomeric forms of the neutral species, and calculated the logP using the current COSMOtherm implementation on the most accurate level. With this approach, COSMO-RS was ranked as the 6st most accurate method (Measured by the mean absolute error (MAE) of 0.57) over all 17 ranked submissions. We achieved a root mean square deviation (RMSD) of 0.78. The largest deviations from experimental values are exhibited by five SAMPL molecules (SM), which seem to be shifted in most SAMPL7 contributions. In context with previous SAMPL challenges, COSMO-RS demonstrates a wide range of applicability and one of the best in class reliability and accuracy among the physical methods.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  1-Octanol–water partition coefficient; COSMO-RS; COSMOtherm; logP prediction

Mesh:

Substances:

Year:  2021        PMID: 34125358     DOI: 10.1007/s10822-021-00395-5

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  10 in total

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Authors:  Caitlin C Bannan; Gaetano Calabró; Daisy Y Kyu; David L Mobley
Journal:  J Chem Theory Comput       Date:  2016-08-01       Impact factor: 6.006

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Journal:  J Chem Inf Model       Date:  2005 Sep-Oct       Impact factor: 4.956

6.  Generating conformer ensembles using a multiobjective genetic algorithm.

Authors:  Mikko J Vainio; Mark S Johnson
Journal:  J Chem Inf Model       Date:  2007-09-25       Impact factor: 4.956

7.  Predicting small-molecule solvation free energies: an informal blind test for computational chemistry.

Authors:  Anthony Nicholls; David L Mobley; J Peter Guthrie; John D Chodera; Christopher I Bayly; Matthew D Cooper; Vijay S Pande
Journal:  J Med Chem       Date:  2008-01-24       Impact factor: 7.446

8.  High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge.

Authors:  Philipp Pracht; Rainer Wilcken; Anikó Udvarhelyi; Stephane Rodde; Stefan Grimme
Journal:  J Comput Aided Mol Des       Date:  2018-08-23       Impact factor: 3.686

9.  Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set.

Authors:  Andreas Klamt; Frank Eckert; Jens Reinisch; Karin Wichmann
Journal:  J Comput Aided Mol Des       Date:  2016-07-26       Impact factor: 3.686

10.  COSMO-RS based predictions for the SAMPL6 logP challenge.

Authors:  Christoph Loschen; Jens Reinisch; Andreas Klamt
Journal:  J Comput Aided Mol Des       Date:  2019-11-26       Impact factor: 3.686

  10 in total
  2 in total

1.  Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

Authors:  Teresa Danielle Bergazin; Nicolas Tielker; Yingying Zhang; Junjun Mao; M R Gunner; Karol Francisco; Carlo Ballatore; Stefan M Kast; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-06-24       Impact factor: 3.686

2.  Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Authors:  Eelke B Lenselink; Pieter F W Stouten
Journal:  J Comput Aided Mol Des       Date:  2021-07-17       Impact factor: 3.686

  2 in total

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