The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.

中文翻译：

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机器学习用于药物开发中的目标发现。

生物活性剂的大分子靶标的发现目前是化学探针和药物先导物的知情设计的瓶颈。通常，针对基因操作细胞系或化学蛋白质组学的活性分析旨在阐明其生物学和解卷积药物靶标网络。通过利用不断增长的大量可公开获得的生物活性数据，学习算法现在提供了一种有吸引力的方法来生成具有统计动机的研究假设，从而优先考虑生化筛选。在这里，我们重点介绍了机器智能在目标识别方面的最新成功，并讨论了药物发现的挑战和机遇。