Research map / Graph learning / Graph self-supervised learning
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use graph-based RL generation with direct property optimization | GCPN · Penalized logP optimization | penalized logP (best): 5.30 -> 7.98 | Helped |
| use chemistry-aware stepwise valency checks during graph generation | GCPN · Penalized logP optimization | validity: 0.4% -> 100.0% | Helped |
| use GCPN for QED optimization | molecular graph generation · QED optimization | QED (best): 0.925 -> 0.948 | Helped |
| use GCPN to target property ranges | molecular graph generation · Property targeting: −2.5 ≤ logP ≤ −2 | success: 11.3% -> 85.5% | Helped |
| use GCPN to target property ranges | molecular graph generation · Property targeting: 5 ≤ logP ≤ 5.5 | success: 7.6% -> 54.7% | Helped |
| use GCPN to target molecular weight | molecular graph generation · Property targeting: 150 ≤ MW ≤ 200 | success: 15.1% -> 76.1% | Helped |
| use GCPN to target molecular weight | molecular graph generation · Property targeting: 500 ≤ MW ≤ 550 | success: 16.0% -> 74.1% | Helped |
| use one shared policy to optimize constrained molecules | GCPN · Constrained penalized logP optimization, δ=0.0 | improvement: 1.91 ± 2.04 -> 4.20 ± 1.28 | Helped |
| add expert imitation pretraining (ablation) | GCPN · Molecule generation training | convergence and performance | Helped |
| retain adversarial reward and expert pretraining to guide generation toward realistic molecules (ablation) | GCPN · Molecule property optimization | molecule realism | Helped |
| increase the number of GCN layers beyond three (ablation) | GCPN · Molecule generation | performance | No clear effect |
| vary the GCN aggregation function (ablation) | GCPN · Molecule generation | performance | No clear effect |
Closest papers by meaning
- On the Scalability of GNNs for Molecular Graphs (NeurIPS 2024 · similarity 0.88)
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- Searching for High-Value Molecules Using Reinforcement Learning and Transformers (ICLR 2024 · similarity 0.86)
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- Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck (AAAI 2025 · similarity 0.85)
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