Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds — including efficacy, pharmacokinetics and safety — need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

小分子药物的发现可以看作是一个具有挑战性的多维问题，其中需要并行优化化合物的各种特性（包括功效，药代动力学和安全性）以提供候选药物。微流体辅助化学合成和生物学测试以及通过反馈分析改善设计假设的人工智能系统等领域的最新进展，现在为将更高的自动化程度引入该过程的各个方面提供了基础。这可能会加快化合物发现和优化的时间范围，并使化学空间的搜索更为有效。但是，这些方法也带来了相当大的概念，技术和组织挑战，并对当前对其周围的炒作表示怀疑。