Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

• URL: http://arxiv.org/abs/2606.01220v1
• Date: Sun, 31 May 2026 13:11:48 GMT
• Title: Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
• Abstract summary: Existing generative approaches often rely on costly post-hoc processing during Sampling or require carefully curated datasets during training.<n>We propose FTDiff, a reinforcement learning fine-tuning framework tailored for diffusion-based molecular generation under structural constraints.<n>We show that FTDiff consistently outperforms prior methods, without requiring expensive post-hoc optimization or intricate data engineering.
• Score: 22.66917124518062
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD). Existing generative approaches, however, often rely on costly post-hoc processing during Sampling or require carefully curated datasets during training, yet still achieve modest gains. These limitations are especially pronounced in multi-objective settings, where balancing conflicting criteria remains a core challenge. To address these challenges, We propose FTDiff, a reinforcement learning fine-tuning framework tailored for diffusion-based molecular generation under structural constraints. To ensure stable and sample-efficient optimization, FTDiff adopts a group relative policy optimization (GRPO) style strategy. Furthermore, FTDiff builds upon a time-free pretrained diffusion model and incorporates a fast sampling mechanism that reduces the number of denoising steps, significantly accelerating both training and inference while maintaining generation quality. By optimizing a fixed threshold-aware reward, FTDiff effectively guides the model to produce valid, diverse, and high- quality molecules that balance multiple drug design objectives. Extensive experiments on benchmark datasets demonstrate that FTDiff consistently outperforms prior methods, without requiring expensive post-hoc optimization or intricate data engineering.

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