DEPPA reformulates the denoising process of pocket-aware diffusion models as a multi-step MDP and applies RL fine-tuning with a coarse scheduler to optimize ligands for binding affinity, drug-likeness, synthesizability and diversity on CrossDocked2020.
Molcraft: structure-based drug design in continuous parameter space.arXiv preprint arXiv:2404.12141
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CAGenMol uses condition-aware discrete diffusion coupled with reinforcement learning to generate valid molecules meeting multiple heterogeneous constraints, outperforming prior methods on binding affinity, drug-likeness, and success rate benchmarks.
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Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization
DEPPA reformulates the denoising process of pocket-aware diffusion models as a multi-step MDP and applies RL fine-tuning with a coarse scheduler to optimize ligands for binding affinity, drug-likeness, synthesizability and diversity on CrossDocked2020.
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CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation
CAGenMol uses condition-aware discrete diffusion coupled with reinforcement learning to generate valid molecules meeting multiple heterogeneous constraints, outperforming prior methods on binding affinity, drug-likeness, and success rate benchmarks.
- From Holo Pockets to Electron Density: GPT-style Drug Design with Density