Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.

机器学习 (ML) 有望解决化学领域的重大挑战，并加速研究假设的生成、改进和/或排序。尽管 ML 工作流程具有总体适用性，但人们通常会发现多种多样的评估研究设计。目前评估技术和指标的异质性导致难以（或不可能）比较和评估新算法的相关性。最终，这可能会延迟化学的大规模数字化，并使方法开发人员、实验人员、审稿人和期刊编辑感到困惑。在这篇 Perspective 中，我们批判性地讨论了一套针对不同类型的基于 ML 的出版物的方法开发和评估指南，强调监督学习。我们提供了来自不同作者和化学学科的各种例子。在考虑研究小组之间不同的可访问性的同时，我们的建议侧重于报告完整性和标准化工具之间的比较。我们的目标是通过建议回顾/前瞻性测试的清单并剖析它们的重要性，进一步提高机器学习的透明度和可信度。我们设想，最佳实践的广泛采用和不断更新将鼓励在知情的情况下使用 ML 解决与化学科学相关的现实问题。