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Table 4 Prediction of the DFT DM by random forests using NBO charges and combining different descriptors

From: Machine learning for the prediction of molecular dipole moments obtained by density functional theory

Models

Training seta

Test set

MAE (D)

R2/RMSE (D)

MAE(D)

R2/RMSE (D)

Ab

0.627

0.757/0.912

0.623

0.774/0.905

Bc

0.616

0.762/0.903

0.611

0.778/0.896

Cd

0.525

0.823/0.780

0.512

0.846/0.752

De

0.522

0.824/0.777

0.509

0.846/0.750

Ef

0.562

0.790/0.850

0.553

0.807/0.838

Fg

0.497

0.837/0.749

0.479

0.860/0.719

  1. aOOB estimation
  2. bRDF pairs NBO charges, PchmDM NBO charges, and DMNBO
  3. cRDF pairs NBO charges, PchmDM NBO charges, geometric CDK, and DMNBO
  4. dRDF pairs NBO charges, PchmDM NBO charges, DMPEOE, and DMNBO
  5. eRDF pairs NBO charges, PchmDM NBO charges, geometric CDK, DMPEOE, and DMNBO
  6. fRDF pairs NBO charges, PchmDM NBO charges, MACCS fingerprints, and DMNBO
  7. gRDF pairs NBO charges, PchmDM NBO charges, MACCS fingerprints, DMPEOE, and DMNBO
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Journal of Cheminformatics

ISSN: 1758-2946

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