用条件感知扩散模型，一步生成符合多重药物特性的分子。
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation
- 结合扩散模型与强化学习，让分子生成过程响应多种复杂约束。
- 在结合力、类药性等指标上优于当前最佳方法，成功率显著提升。
- 适合需要同时优化多个目标的药物分子设计人员使用。
定向分子生成需满足蛋白质-配体相容性及多目标类药性等异构约束，但现有方法常孤立优化，难以调和冲突目标（如亲和力与安全性），且在不可微的化学空间中难以保持结构有效性。为此，我们提出CAGenMol，一种基于分子序列的条件感知离散扩散框架，将分子设计建模为受异构结构与性质信号引导的条件去噪过程。通过将离散扩散与强化学习结合，模型在不破坏化学有效性的前提下，使生成轨迹对不可微目标实现对齐。非自回归的扩散语言模型结构支持推理时对分子片段进行迭代优化。在结构约束、属性约束及双约束基准上的实验表明，该方法在结合力、类药性及成功率方面持续超越当前最优方法，验证了其有效性。
原文摘要 · Abstract (English)
Goal-directed molecular generation requires satisfying heterogeneous constraints such as protein--ligand compatibility and multi-objective drug-like properties, yet existing methods often optimize these constraints in isolation, failing to reconcile conflicting objectives (e.g., affinity vs. safety), and struggle to navigate the non-differentiable chemical space without compromising structural validity. To address these challenges, we propose CAGenMol, a condition-aware discrete diffusion framework over molecular sequences that formulates molecular design as conditional denoising guided by heterogeneous structural and property signals. By coupling discrete diffusion with reinforcement learning, the model aligns the generation trajectory with non-differentiable objectives while preserving chemical validity and diversity. The non-autoregressive nature of diffusion language model further enables iterative refinement of molecular fragments at inference time. Experiments on structure-conditioned, property-conditioned, and dual-conditioned benchmarks demonstrate consistent improvements over state-of-the-art methods in binding affinity, drug-likeness, and success rate, highlighting the effectiveness of our framework.
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