Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization

• URL: http://arxiv.org/abs/2605.17693v1
• Date: Sun, 17 May 2026 23:21:24 GMT
• Title: Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization
• Abstract summary: We present DEPPA, a novel structure-based molecule optimization (SBMO) approach for fine-tuning a pre-trained pocket-aware diffusion model.<n> DEPPA enables optimization over multiple properties, including binding affinity, drug-likeness, synthesizability and diversity.
• Score: 6.296632663190645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-based drug design has been accelerated by pocket-aware 3D generative models, yet most methods primarily fit the training distribution and may fall short of satisfying multiple properties required in real-world therapeutic drug discovery. Recently, increasing attention has focused on structure-based molecule optimization (SBMO), which targets fine-grained control over multiple specified molecular properties. In this paper, we present DEPPA, a novel SBMO approach building upon Denoising Diffusion Policy Optimization for fine-tuning a pre-trained pocket-aware diffusion model via reinforcement learning. DEPPA enables optimization over multiple properties, including binding affinity, drug-likeness, synthesizability and diversity. We formulate the reverse denoising process of the pretrained pocket-aware diffusion model as a multi-step Markov Decision Process, where the desired properties that serve as reward signals are evaluated on the final generated ligand molecules. DEPPA incorporates a coarse denoising scheduler during the RL fine-tuning to achieve efficient and effective molecule optimization. Experimental results on the CrossDocked2020 benchmark demonstrate that DEPPA outperforms baselines in binding affinity (Vina Score -8.5 kcal/mol), drug-likeness and diversity while exhibiting competitive performance in synthesizability. The source code is available at https://github.com/xy9485/DePPA .

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