Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream .

中文翻译：

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DockStream：增强从头分子设计的对接包装器

最近，我们发布了从头设计平台REINVENT 2.0版本。这种改进和扩展的迭代支持更多的功能和评分功能组件，这使得定制和量身定制的协议能够最大限度地发挥小分子药物发现项目的影响。生成模型的一个主要障碍是生产活性化合物，其中预测（QSAR）模型已被应用于丰富目标活性。然而，QSAR 模型本质上受到其适用范围的限制。为了克服这些限制，我们为 REINVENT 引入了基于结构的评分组件。DockStream 是一种灵活的独立分子对接包装器，可提供对一系列配体嵌入器和对接后端的访问。使用 DockStream 中提供的基准测试和分析工作流程，可以自动执行各种对接配置和后续分析。根据目标的不同，对接算法的性能差异很大，基准测试和分析工作流程提供了一个简化的解决方案来识别高效的对接配置。我们表明，信息丰富的对接配置可以通知 REINVENT 代理进行优化，以使用公共数据提高对接分数。对接激活后，REINVENT 能够保留结合位点中的关键相互作用，丢弃不适合结合腔的分子，利用未使用的（子）口袋，并提高支架跳跃场景中的整体性能。该代码可在 https://github.com/MolecularAI/DockStream 上免费获取。