Purpose

To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.

Methods

Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >10⁵ drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits.

Results

Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae.

Conclusions

This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web.

Graphical Abstract

中文翻译：

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机器学习平台，用于发现淋病奈瑟氏球菌的新型生长抑制剂。

目的

通过朴素的贝叶斯建模方法，通过预测新型小分子生长抑制剂来推进淋病奈瑟氏球菌的基础生物学和翻译研究。

方法

检查和整理来自公众可用的ChEMBL网站上的淋病奈瑟氏球菌小分子生长抑制数据的数据，从而为建立机器学习模型提供了训练。在工作流程中使用了朴素的贝叶斯细菌生长抑制模型，用于从大于10 ^5个药物样小分子的商品库中预测出与这种细菌具有全球健康相关性的新型抗菌剂。后续工作涉及对预测的经验评估和命中的验证。

结果

具体而言，发现了两个小分子，它们表现出有希望的活性谱并代表针对淋病奈瑟氏球菌的新化学型。

结论

据我们所知，这是成功预测该细菌新型生长抑制剂的第一种机器学习方法。为了协助化学工具和药物发现领域，我们将精选的培训集作为补充材料的一部分提供，并且可以通过网络访问贝叶斯模型。

图形概要