The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

中文翻译：

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迈向可重复的计算药物发现


实验的可重复性一直是进一步科学进步的长期障碍。计算方法由于其在数据收集、预处理、分析和推理方面的多方面用途，在药物发现工作中发挥了重要作用。本文深入介绍了计算药物发现的可重复性。本综述探讨了以下主题：（1）可重复研究的最新进展，（2）研究文档（例如电子实验室笔记本，Jupyter笔记本等），（3）可重复研究科学（即与可复制性、可重用性和可靠性等相关概念的比较和对比），(4) 计算药物发现中的模型开发，(5) 模型开发和部署的计算问题，(6) 简化计算药物发现协议的用例场景。在计算学科中，共享用于数值计算的数据和编程代码已成为常见做法，不仅可以促进可重复性，而且可以促进协作（即通过引入新想法、增加数据、增强代码来进一步推动项目） ， ETC。）。因此，计算药物设计领域将不可避免地采用开放的方法来收集、管理和共享数据/代码。