Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (M^pro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

中文翻译：

---

通过交互生成人工智能和分子对接来导航化学空间

在这里，我们报告了一个简单的结构感知框架的实现和应用，以生成特定于目标的筛选库。我们的方法将生成人工智能 (AI) 的进步与传统的分子对接相结合，以探索以生物分子目标活性位点的独特物理化学特性为条件的化学空间。作为演示，我们使用我们称为样本和坞站的框架来构建细胞周期蛋白依赖性激酶 2 (CDK2) 和主要蛋白酶的活性位点 (M ^pro) 的 SARS-CoV-2 病毒。我们设想样品和底座框架可用于生成特定目标化学空间的理论图，从而提供有关其分子识别特征的信息。