The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability. The presented workflow tackles, for the first time, the challenge of data scarcity particularly focusing rare modes-of-action. In addition, the workflow and associated dataset provide new standards in the definition and application of criteria to rationalize drug candidate selection addressing important gaps in cheminformatics as well as computational and medicinal chemistry.

中文翻译：

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计算机辅助模式评分（C@PS）：一种新颖的化学信息学工作流程，用于预测具有罕见作用模式的配体


潜在药理药物靶标的识别、建立和探索是药物开发流程的主要步骤。目标验证需要具有多种功能的多种化学工具，例如抑制剂、激活剂和其他调节剂。特别是具有罕见作用模式的工具可以对感兴趣的靶标（例如通道、酶、受体或转运蛋白）进行适当的动力学和功能表征。尽管如此，功能创新是可专利性和商业利用的主要标准，这可能会带来治疗益处。不幸的是，关于新的、不可成药或几乎不可成药的靶标的数据很少，并且大多数仅可用于主流作用模式（例如抑制）。在这里，我们提出了一种新颖的化学信息学工作流程——计算机辅助模式评分（C@PS）——专门设计用于将其预测能力投射到未知的适用领域。所提出的工作流程首次解决了数据稀缺的挑战，特别是关注罕见的行动模式。此外，工作流程和相关数据集在标准的定义和应用方面提供了新的标准，以合理化候选药物的选择，解决化学信息学以及计算和药物化学方面的重要差距。