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 first 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. Our proposed 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 and potential.

翻译：结构化分子优化（SBMO）旨在针对蛋白质靶点优化同时具有连续坐标和离散类型的分子。鉴于梯度引导在图像生成领域的显著成功，将其应用于生成模型是一个前景广阔的方向，但如何引导离散数据并避免模态间不一致性仍是挑战。为此，我们通过贝叶斯推理构建了一个连续可微空间，提出了首个基于梯度的SBMO框架——分子联合优化（MolJO），该框架能在保持SE(3)等变性的同时实现跨模态联合梯度引导。我们引入了一种新颖的后向校正策略，通过在历史滑动窗口内进行优化，实现了探索与利用之间的动态平衡。在CrossDocked2020基准测试中，MolJO取得了最先进的性能（成功率51.3%、Vina对接得分-9.05、合成可达性0.78），其成功率较现有梯度方法提升4倍以上，“Me-Better”比率达到3D基准方法的2倍。此外，我们将MolJO拓展至多目标优化、R基团优化和骨架跃迁等药物设计挑战性任务，进一步证明了其通用性和应用潜力。