Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
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Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.
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Synergistic Benefits of Joint Molecule Generation and Property Prediction
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
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Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.