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Sample-efficient Multi-objective Molecular Optimization with GFlowNets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a preference-conditioned hypernetwork rather than concatenation or FiLM conditioning (ablation) | GFlowNet on the synthetic molecular optimization task · Synthetic GSK3β + JNK3 | HV: Concat-GFN: 0.534 ± 0.069; FiLM-GFN: 0.431 ± 0.045; HN-GFN: 0.550 ± 0.074 | Helped |
| Use one preference-conditioned HN-GFN instead of separate preference-specific GFlowNets | GFlowNet on the synthetic molecular optimization task · Synthetic GSK3β + JNK3 | HV: PS-GFN: 0.545 ± 0.055; HN-GFN: 0.550 ± 0.074 | No clear effect |
| Use HN-GFN with hindsight-like off-policy sharing (ablation) | HN-GFN within MOBO · MOBO first-round training | average Top-20 reward | Helped |
| Increase the hindsight mixture proportion γ (ablation) | HN-GFN within MOBO · MOBO first-round training | average Top-20 reward | Helped |
| Skew the preference sampling distribution toward harder properties (ablation) | HN-GFN · GSK3β + JNK3 + QED + SA MOBO | HV: 0.312 ± 0.039 -> 0.416 ± 0.023 | Helped |
| Replace weighted-sum scalarization with Tchebycheff scalarization (ablation) | HN-GFN · GSK3β + JNK3 + QED + SA MOBO | HV: 0.416 ± 0.023 -> 0.304 ± 0.075 | Hurt |
| Replace evidential regression with Deep Ensembles (ablation) | HN-GFN MOBO surrogate · GSK3β + JNK3 MOBO | HV: 0.669 ± 0.061 -> 0.583 ± 0.103 | Hurt |
| Replace evidential regression with a Tanimoto-kernel GP (ablation) | HN-GFN MOBO surrogate · GSK3β + JNK3 MOBO | Div: 0.793 ± 0.007 -> 0.739 ± 0.008 | Hurt |
| Replace evidential regression with Deep Ensembles (ablation) | HN-GFN MOBO surrogate · GSK3β + JNK3 + QED + SA MOBO | HV: 0.416 ± 0.023 -> 0.355 ± 0.048 | Hurt |
| Replace evidential regression with a Tanimoto-kernel GP (ablation) | HN-GFN MOBO surrogate · GSK3β + JNK3 + QED + SA MOBO | HV: 0.416 ± 0.023 -> 0.421 ± 0.037 | No clear effect |
| Train HN-GFN using weighted-sum rather than Tchebycheff scalarization (ablation) | HN-GFN · GSK3β + JNK3 + QED + SA MOBO | Div: 0.732 ± 0.014 -> 0.738 ± 0.009 | No clear effect |
Closest papers by meaning
- Generative Flow Networks for Lead Optimization in Drug Design (Student Abstract) (AAAI 2025 · similarity 0.88)
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- Genetic-guided GFlowNets for Sample Efficient Molecular Optimization (NeurIPS 2024 · similarity 0.87)
- Order-Preserving GFlowNets (ICLR 2024 · similarity 0.87)
- SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints (ICLR 2025 · similarity 0.87)
- RGFN: Synthesizable Molecular Generation Using GFlowNets (NeurIPS 2024 · similarity 0.87)
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