用梯度引导分子优化，提升药物设计效率与多样性
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
- 构建连续可微空间，实现坐标与类型联合优化
- 在CrossDocked2020上成功率达51.3%，较基线提升4倍
- 适用于多目标、R基团替换等复杂药物设计任务
基于结构的分子优化（SBMO）旨在针对蛋白靶点优化分子的连续坐标与离散类型。尽管梯度引导在图像生成中表现优异，但对离散数据的引导仍具挑战，且易引发模态不一致。为此，本文提出基于贝叶斯推断的连续可微空间，构建分子联合优化框架MolJO，实现跨模态梯度协同并保持SE(3)等变性。引入滑动窗口内的反向修正策略，平衡探索与利用。MolJO在CrossDocked2020基准上取得51.3%成功率、Vina打分-9.05、SA值0.78，较梯度基线成功率提升4倍，优于3D基线的“我更优”比率达2倍。此外，该方法扩展至多目标优化、R基团优化及骨架跃迁等复杂任务，展现强大通用性。代码已开源。
原文摘要 · Abstract (English)
Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3%, Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility. Code is available at https://github.com/AlgoMole/MolCRAFT.
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