Literature DB >> 34164769

SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules.

Basak Koca Fındık1, Zeynep Pinar Haslak1, Evrim Arslan1, Viktorya Aviyente2.   

Abstract

The physicochemical properties of a drug molecule determine the therapeutic effectiveness of the drug. Thus, the development of fast and accurate theoretical approaches for the prediction of such properties is inevitable. The participation to the SAMPL7 challenge is based on the estimation of logP coefficients and pKa values of small drug-like sulfonamide derivatives. Thereby, quantum mechanical calculations were carried out in order to calculate the free energy of solvation and the transfer energy of 22 drug-like compounds in different environments (water and n-octanol) by employing the SMD solvation model. For logP calculations, we studied eleven different methodologies to calculate the transfer free energies, the lowest RMSE value was obtained for the M06L/def2-TZVP//M06L/def2-SVP level of theory. On the other hand, we employed an isodesmic reaction scheme within the macro pKa framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pKa values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  Computer-aided drug design; DFT; LogP; SAMPL7; Solvation free energies; pK a

Mesh:

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Year:  2021        PMID: 34164769     DOI: 10.1007/s10822-021-00402-9

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


  50 in total

1.  Rapid prediction of solvation free energy. 3. Application to the SAMPL2 challenge.

Authors:  Enrico O Purisima; Christopher R Corbeil; Traian Sulea
Journal:  J Comput Aided Mol Des       Date:  2010-04-06       Impact factor: 3.686

2.  Prediction of SAMPL2 aqueous solvation free energies and tautomeric ratios using the SM8, SM8AD, and SMD solvation models.

Authors:  Raphael F Ribeiro; Aleksandr V Marenich; Christopher J Cramer; Donald G Truhlar
Journal:  J Comput Aided Mol Des       Date:  2010-04-01       Impact factor: 3.686

3.  Prediction of SAMPL3 host-guest binding affinities: evaluating the accuracy of generalized force-fields.

Authors:  Hari S Muddana; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2012-01-25       Impact factor: 3.686

4.  Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4.

Authors:  Gerhard König; Frank C Pickard; Ye Mei; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2014-02-07       Impact factor: 3.686

5.  Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge.

Authors:  Lars Sandberg
Journal:  J Comput Aided Mol Des       Date:  2014-02-19       Impact factor: 3.686

6.  Prediction of free energies of hydration with COSMO-RS on the SAMPL4 data set.

Authors:  Jens Reinisch; Andreas Klamt
Journal:  J Comput Aided Mol Des       Date:  2014-01-14       Impact factor: 3.686

7.  SAMPL4, a blind challenge for computational solvation free energies: the compounds considered.

Authors:  J Peter Guthrie
Journal:  J Comput Aided Mol Des       Date:  2014-04-06       Impact factor: 3.686

8.  Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.

Authors:  Mehtap Işık; Teresa Danielle Bergazin; Thomas Fox; Andrea Rizzi; John D Chodera; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2020-02-27       Impact factor: 3.686

Review 9.  Blind prediction of solvation free energies from the SAMPL4 challenge.

Authors:  David L Mobley; Karisa L Wymer; Nathan M Lim; J Peter Guthrie
Journal:  J Comput Aided Mol Des       Date:  2014-03-11       Impact factor: 3.686

Review 10.  The SAMPL4 host-guest blind prediction challenge: an overview.

Authors:  Hari S Muddana; Andrew T Fenley; David L Mobley; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2014-03-06       Impact factor: 3.686

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  1 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

  1 in total

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