AMORTIX uses group-normalized rewards in reinforcement learning to train an amortized Graph Transformer that optimizes constrained molecules in one forward pass and outperforms baselines on kinase inhibitor and prodrug design tasks.
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Amortized Molecular Optimization via Group Relative Policy Optimization
AMORTIX uses group-normalized rewards in reinforcement learning to train an amortized Graph Transformer that optimizes constrained molecules in one forward pass and outperforms baselines on kinase inhibitor and prodrug design tasks.