Table 2.
Comparison of generator quality metrics and various other parameters of proposed model with other baseline architectures.
| Architecture Type | Model Name | Dataset used | Size of Molecule | No.of Trained Molecules | No.of Generated Molecules | Generator quality metrics | Task |
|---|---|---|---|---|---|---|---|
| RNN and AE based Architecure | Grammar VAE [50] | ZINC | <39 heavy atoms | 250,000 | 100,000 | 7.2% (V) | Penalized logP |
| SD VAE [51] | ZINC | <39 heavy atoms | 250,000 | 100,000 | 43.5% (V) | Penalized logP | |
| AAE [52] | ChEMBL | <121 characters | 1.3 million | no data | 77.4% (V) | Drug analog generation | |
| ECAAE [53] | ZINC | <58 characters | 1.8 million | 10000 | No data | Structural analogs | |
| GAN and RNN based Architecure | ORGAN [54] | QM9 | <52 characters | 5000 | No data | 80.3% (V) | nlogP |
| ORGANIC [55] | QM9 | <10 heavy atoms | 5000 | No data | 0.2–99%(V), 86% (N) | nQED | |
| ATNC [56] | ChemDiv | <91 characters | 15000 | 157986 | 72%(V),77% (N) | No.of unique heterocycles | |
| RANC [57] | ChemDiv | <91 characters | 15000 | 896000 | 58%(V),48% (N) | No.of unique heterocycles | |
| RNN based Architecure with RL | REINVENT [58] | ChEMBL | 10 - 50 heavy atoms | 1.5million | 12800 | 94% (V), 90% (N) | Drug analog generation |
| ReLeaSE [59] | ChEMBL | No data | 1.5 million | 1 million | 95%(V),95.3% (N) | Inhibitor of JAK2 | |
| ChemTS [60] | ZINC | No data | 250,000 | No data | No data | Penalized logP | |
| RNN based Architecure | Segler et al. [33] | ChEMBL | No data | 1.4 million | 976,327 | 97.7% (V), 89.4% (N) | Plasmodium falciparum,5 - HT2A |
| Bjerrum et al. [61] | ZINC | No data | 1,611,889 | 50000 | 98% (V), 63% (N) | Retro-synthetic route of easy/medium/hard group | |
| Gupta et al. [16] | ChEMBL | 34-74 characters | 541,555 | 30107 | 93% (V), 92% (N) | PPARs, Trypsin | |
| Ours | ChEMBL and MOSES | 34-128 characters | 2.9 million | 10000 | 70.50% (V), 99.83% (N),98.99% (U) | Inhibitor of 3CLPro (novel Corona virus main protease) |
Qvalid, QUnique, QNovel are represented as V, U, N respectively. Autoencoders (AE), Adversarial autoencoder (AAE), Generative Adversarial network (GAN).