Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA.
Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
Pharm Res. 2020 Jul 13;37(7):141. doi: 10.1007/s11095-020-02876-y.
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.
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 >10 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits.
Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae.
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.
通过基于朴素贝叶斯建模方法预测新型小分子生长抑制剂，推进淋病奈瑟菌的基础生物学和转化研究。
从公开的 ChEMBL 网站上检查和整理淋病奈瑟菌小分子生长抑制数据，为构建机器学习模型提供了一个训练集。利用朴素贝叶斯模型对细菌生长抑制进行建模，作为工作流程的一部分，从一个超过 10 种药物样小分子的商业文库中预测针对这种具有全球健康相关性的细菌的新型抗菌剂。后续的工作包括对预测结果进行实证评估和验证命中结果。
具体来说，发现了两种具有有前景的活性特征的小分子，它们代表了针对淋病奈瑟菌的新型化学型药物。
据我们所知，这是第一个成功预测这种细菌新型生长抑制剂的机器学习方法。为了协助化学工具和药物发现领域，我们将经过整理的训练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。图表摘要。
