Research map / Graph learning / Graph self-supervised learning
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MIMOSA instead of GA for joint PLogP and QED optimization | Molecule optimization · PLogP + QED optimization | success rate: 24.9% -> 32.0% | Helped |
| Use MIMOSA instead of GA for joint PLogP and DRD optimization | Molecule optimization · PLogP + DRD optimization | success rate: 29.3% -> 43.7% | Helped |
| Use MIMOSA instead of GA for QED optimization | Molecule optimization · QED optimization | success rate: 42.5% -> 47.8% | Helped |
| Use MIMOSA instead of VJTNN for DRD optimization | Molecule optimization · DRD optimization | success rate: 40.5% -> 48.3% | Helped |
| Use MIMOSA instead of GA for PLogP optimization | Molecule optimization · PLogP optimization | success rate: 92.8% -> 94.0% | Helped |
Closest papers by meaning
- MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction (ICML 2025 · similarity 0.87)
- DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform (NeurIPS 2025 · similarity 0.86)
- InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization (ICLR 2025 · similarity 0.86)
- Multi-Objective Molecular Design Through Learning Latent Pareto Set (AAAI 2025 · similarity 0.85)
- Molecule Design by Latent Prompt Transformer (NeurIPS 2024 · similarity 0.85)
- Searching for High-Value Molecules Using Reinforcement Learning and Transformers (ICLR 2024 · similarity 0.85)
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