Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

中文翻译：

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通过靶点评估、深度学习和自动化实验室加速靶点识别：IRAK1 中的前瞻性验证


通过应用生物医学知识分析、人工智能驱动的虚拟筛选和机器人云实验室系统，可以改变靶点识别和苗头化合物识别。然而，很少有前瞻性研究评估这种综合方法的有效性。我们将内部开发的目标评估 （SpectraView） 和深度学习驱动的虚拟筛选 （HydraScreen） 工具与专为超高通量筛选设计的自动化机器人云实验室协同集成，使我们能够通过实验验证这些平台。通过使用我们的目标评估工具选择 IRAK1 作为我们调查的重点，我们前瞻性地验证了我们基于结构的深度学习模型。我们可以在排名前 1% 的化合物中识别出 23.8% 的 IRAK1 命中。该模型优于传统的虚拟筛选技术，并提供配体姿态置信度评分等高级功能。同时，我们从我们的化合物库中鉴定了三种有效的（纳摩尔）支架，其中 2 种代表了 IRAK1 的新型候选物，并有望在未来开发。这项研究为 SpectraView 和 HydraScreen 提供了令人信服的证据，可显著加速靶标识别和苗头化合物发现过程。通过利用 Ro5 的 HydraScreen 和 Strateos 的自动化实验室对 IRAK1 进行苗头化合物识别，我们展示了使用 HydraScreen 进行 AI 驱动的虚拟筛选如何提供高苗头化合物发现率并降低实验成本。我们提出了一个创新平台，该平台利用基于知识图谱的生物医学数据分析和与机器人云实验室集成的 AI 驱动的虚拟筛选。 通过公正的前瞻性评估，我们展示了 HydraScreen 在 IRAK1 中苗头化合物识别的虚拟和高通量筛选中的可靠性和稳健性。我们的平台和创新工具可以加快药物发现的早期阶段。