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Table 3 Models’ performance (root-mean-square error) on lipophilicity database

From: A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Dataset (size)

Model

RMSE

Lipophilicity (4200)

RF

0.824 ± 0.041

MPN (Deepchem)a

0.630 ± 0.059

MPN (Deepchem)b

0.652 ± 0.061

MPN

0.630 ± 0.059

SAMPN

0.579 ± 0.036

Multi-MPN

0.594 ± 0.039

Multi-SAMPN

0.571 ± 0.032

Water solubility (1311)

RF

1.096 ± 0.092

MPN (Deepchem-1128)a

0.580 ± 0.030

MPN (Deepchem)b

0.676 ± 0.022

MPN

0.694 ± 0.050

SAMPN

0.688 ± 0.057

Multi-MPN

0.674 ± 0.074

Multi-SAMPN

0.661 ± 0.063

  1. Italics represents the best performance in the results
  2. aValues were reported in [16]. In the lipophilicity prediction, we use the same dataset with Deepchem. In the water solubility prediction, our used dataset is larger than Deepchem used (1128 molecules)
  3. bValues were calculated from the same data and the same stratified cross-validation protocol in our work
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Journal of Cheminformatics

ISSN: 1758-2946

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