Table 6 Results of the constrained optimization for Junction Tree Variational Autoencoder [30] (JT-VAE), Graph Convolutional Policy Network [35] (GCPN) and Mol-CycleGAN
From: Mol-CycleGAN: a generative model for molecular optimization
\(\delta \) | JT-VAE | GCPN | Mol-CycleGAN | ||||||
|---|---|---|---|---|---|---|---|---|---|
Improvement | Similarity | Success (%) | Improvement | Similarity | Success (%) | Improvement | Similarity | Success (%) | |
0 | 1.91 ± 2.04 | 0.28 ± 0.15 | 97.5 | 4.20 ± 1.28 | 0.32 ± 0.12 | 100.0 | 8.30 ± 1.98 | 0.16 ± 0.09 | 99.75 |
0.2 | 1.68 ± 1.85 | 0.33 ± 0.13 | 97.1 | 4.12 ± 1.19 | 0.34 ± 0.11 | 100.0 | 5.79 ± 2.35 | 0.30 ± 0.11 | 93.75 |
0.4 | 0.84 ± 1.45 | 0.51 ± 0.10 | 83.6 | 2.49 ± 1.30 | 0.47 ± 0.08 | 100.0 | 2.89 ± 2.08 | 0.52 ± 0.10 | 58.75 |
0.6 | 0.21 ± 0.75 | 0.69 ± 0.06 | 46.4 | 0.79 ± 0.63 | 0.68 ± 0.08 | 100.0 | 1.22 ± 1.48 | 0.69 ± 0.07 | 19.25 |