AI 中文总结
本文提出集成条件引导框架，通过同时优化构象集成模式与性质，实现多条件3D分子生成，在双靶点结合剂和活性状态选择性激动剂设计中优于单状态条件化。
AI 中文摘要
分子设计通常被视为寻找能够采用单一生物活性构象的分子的过程。实际上，分子占据一个构象分布，而决定候选分子是否可行的许多性质取决于该分布，而非任何单一构象体。我们将分子设计重新定义为对分子构象集成的模式和性质的双重优化，其中模式可以表示为形状、药效团图谱或蛋白口袋，而性质是在整个分布上计算的聚合标量。为实现这一目标，我们引入了集成条件引导（ensemble-conditioned guidance）框架，该框架同时在这两个轴上条件化3D分子生成模型。模式条件在推理时通过组合每种条件下产生的向量场进行自适应组合。条件可以被定向或避免，跨模态混合，并以任意数量组合，从而允许使用单个训练模型表达广泛的设计任务。我们引入了自适应对称学习，以允许来自不同参考系的条件进行组合，并将我们的生成框架扩展至支持灵活大小的生成。我们在新的多模式条件和集成性质优化基准上进行了评估，并将该框架应用于两个实际的药物发现任务：双靶点结合剂设计和活性状态选择性激动剂设计，在这两种情况下，对额外状态的条件化均优于单状态条件化，改善了期望结果。
英文摘要
Molecular design is typically approached as a problem of finding molecules which can adopt a single bioactive conformation. In reality, molecules occupy a distribution over conformations, and many of the properties which determine whether a candidate is viable depend on that distribution rather than on any single conformer. We reframe molecular design as an optimisation of both the modes and properties of molecules' conformational ensembles, where modes can be represented as shapes, pharmacophore profiles or protein pockets, and properties are aggregate scalars computed over the whole distribution. To realise this we introduce ensemble-conditioned guidance, a framework which conditions 3D molecular generative models on both axes simultaneously. Mode conditions are composed adaptively at inference by combining the vector fields produced under each condition. Conditions may be targeted or avoided, mixed across modalities and combined in arbitrary numbers, allowing a wide range of design tasks to be expressed with a single trained model. We introduce adaptive symmetry learning to allow conditions from different reference frames to be composed, and extend our generative framework to enable flexible-size generation. We evaluate on new benchmarks for multi-mode conditioning and ensemble property optimisation, and apply the framework to two practical drug discovery tasks, dual-target binder design and active-state-selective agonist design, where in both cases conditioning on the additional state improves the desired outcome over single-state conditioning.
CommentsCode available at: https://github.com/rssrwn/ensemble-cond-design datasets and checkpoints available at: https://zenodo.org/records/22485204
