SmilesGEN uses dual VAEs to jointly model drug structures and transcriptional responses, generating molecules with higher validity, novelty, and similarity to known ligands than prior methods.
N.; Duvenaud, D.; Hern \'a ndez-Lobato, J
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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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Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning
SmilesGEN uses dual VAEs to jointly model drug structures and transcriptional responses, generating molecules with higher validity, novelty, and similarity to known ligands than prior methods.
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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.