Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions-structural similarity, binding profiles and their network effects across pathways and molecular interaction maps-to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.

中文翻译：

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用于大规模深度筛选化合物效果评估的多维计算管道：老化相关化合物的计算机案例研究。

在当前的化合物分析方法中，设计替代方法来有效筛选化学物质的功效是一项具有挑战性但又不可或缺的任务。特别是，不断增加的监管限制强调需要开发先进的计算管道来评估化合物的功效，作为减少和/或取代体内实验的替代手段。在这里，我们提出了一种创新的计算管道，通过基于多个维度（结构相似性、结合概况及其跨途径和分子相互作用图的网络效应）对化合物进行分析和聚类，以生成可测试的假设，用于大规模评估化合物药理学景观以及确定现象学过程的潜在功效机制。此外，我们阐明了该管道在抗衰老相关化合物筛选中的应用，以根据候选化合物的结构、对接概况和对与细胞活力相关的基本代谢/分子途径的网络效应对候选化合物进行聚类，突出了对化合物的新兴见解基于多维深屏管道的活动。