基于云应用和协作空间的开放式创新平台:溶解度预测模型开发的案例研究

IF 0.4 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Chem-Bio Informatics Journal Pub Date : 2020-04-27 DOI:10.1273/cbij.20.5
Tsuyoshi Esaki, Keiko Kumazawa, Kazutoshi Takahashi, Reiko Watanabe, Tomohide Masuda, Hirofumi Watanabe, Yugo Shimizu, A. Okada, Seisuke Takimoto, Tomohiro Shimada, Kazuyoshi Ikeda
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引用次数: 1

摘要

近年来,随着信息科学新技术的出现,利用生物和化学实验数据的开放式创新和协同药物发现研究积极开展。化学-生物信息学学会青年研究员协会(“CBI Wakate”)利用Slack构建了一个在线讨论空间,并提供了一个基于云的协作平台,研究人员可以在其中自由讨论具体问题,旨在提高跨部门的技术和知识交流水平。在这个平台上,我们创建了三个渠道——数据集、模型评估和脚本——不同背景的参与者共同开发了溶解度预测的解决方案。在数据集通道中,我们交换了我们的知识和化学-生物信息学杂志,Vol.20, pp.5-18(2020) 6使用原始数据集的化学描述符进行计算的方法,并讨论了用于制药目的的数据集改进方法。我们还开发了一个协议,通过使用ChEMBL数据库来评估溶解度预测模型在药物发现中的适用性,并在云上的用户之间共享数据集。在模型评价通道中,我们讨论了预测模型用于日常药物发现研究的必要条件。我们检查了这些讨论对脚本开发的影响,并提出了未来的改进建议。这项研究提供了一个新的基于云的开放协作的例子,可以用于药物发现早期阶段的各种项目。
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Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development
In recent years, with the emergence of new technologies employing information science, open innovation and collaborative drug discovery research, utilizing biological and chemical experimental data, have been actively conducted. The Young Researcher Association of Chem-Bio Informatics Society (“CBI Wakate”) has constructed an online discussion space using Slack and provided a cloud-based collaborative platform in which researchers have freely discussed specific issues and aimed at raising the level of cross-sectoral communication regarding technology and knowledge. On this platform, we created three channels—dataset, model evaluation and scripts—where participants with different backgrounds co-developed a solution for solubility prediction. In the dataset channel, we exchanged our knowledge and Chem-Bio Informatics Journal, Vol.20, pp.5–18 (2020) 6 methodology for calculations using the chemical descriptors for the original dataset and also discussed methods to improve the dataset for pharmaceutical purposes. We have also developed a protocol for evaluating the applicability of solubility prediction models for drug discovery by using the ChEMBL database and for sharing the dataset among users on the cloud. In the model evaluation channel, we discussed the necessary conditions for the prediction model to be used in daily drug discovery research. We examined the effect of these discussions on script development and suggested future improvements. This study provides an example of a new cloud-based open collaboration that can be useful for various projects in the early stage of drug discovery.
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来源期刊
Chem-Bio Informatics Journal
Chem-Bio Informatics Journal BIOCHEMISTRY & MOLECULAR BIOLOGY-
CiteScore
0.60
自引率
0.00%
发文量
8
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