解决扩散模型生成分子时训练与推理不一致的问题。
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
- 引入自适应采样与伪分子估计，动态对齐生成过程。
- 在CrossDocked2020上得分优于现有方法，生成分子更接近药物结构。
- 适合药物设计、生成模型研究者使用。
基于深度生成模型的结构驱动分子生成范式正快速发展，但现有方法仍受限于暴露偏差、误差累积及活性悬崖处理不佳等问题。本文提出DiffGap框架，融合自适应采样与伪分子估计，实现3D分子生成中训练目标与推理动态的对齐。通过动态调整去噪步骤以匹配真实生成轨迹，模型可在训练阶段预先适应输入偏差。温度退火模块控制对齐强度，保障数据分布稳定学习。在CrossDocked2020基准测试中，DiffGap在对接分数和结合亲和力上均优于现有方法，生成分子具有更高保真度。本工作为生成训练与推理机制的统一提供了系统性方法，助力基于结构的药物发现加速。代码已开源：https://github.com/neusymlab/DiffGap。
原文摘要 · Abstract (English)
The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. However, these generative models for molecular design remain constrained by exposure bias, error accumulation, and suboptimal handling of activity cliffs. Here, we introduce DiffGap, a diffusion-based framework that integrates adaptive sampling and pseudo-molecule estimation to bridge the gap between training objectives and inference dynamics in 3D molecule generation. By dynamically aligning intermediate denoising steps with realistic generation trajectories, DiffGap enables the diffusion model to adapt to input biases in advance during the training phase. A temperature annealing module further controls the aligning strength of the adaptive alignment process, ensuring stable learning of the data distribution. Evaluated on the CrossDocked2020 benchmark, DiffGap outperforms existing methods in docking scores and binding affinity, demonstrating superior fidelity in generating drug-like molecules. Our work establishes a principled approach to harmonize generative training with inference mechanics, offering a robust computational toolkit for accelerating structure-based therapeutic discovery. The source code of DiffGap is available at https://github.com/neusymlab/DiffGap.
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