QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ∼200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ∼76%, representing a 1.5–2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available.随着新型及耐药菌株持续构成新兴健康威胁，研发新型抗菌制剂已成为当务之急。这既包括对现有抗菌母核进行结构优化，也涵盖新型抗菌母核的发掘。本研究旨在采用贝叶斯分类定量构效关系（Quantitative Structure-Activity Relationship, QSAR）方法，快速筛选化学库中预测具有抗菌活性的化合物。为此，我们构建了包含317种已知抗菌化合物的数据集，以及另一组源自215项针对不同细菌物种的PubChem生物测定（PubChem Bioassay）的多样化、经过充分验证的非抗菌化合物数据集。我们基于结构指纹与理化性质描述符构建了贝叶斯分类模型，并在独立测试集上实现了84%的准确率与86%的精确率，可有效识别抗菌化合物。为验证该模型在虚拟筛选中的实际应用价值，我们对一组包含约20万个化合物的独立数据集进行了筛选。结果显示，该模型可筛选出PubChem生物测定活性物中的优质命中化合物，准确率最高可达约76%，富集倍数达1.5至2倍。筛选得到的优质命中化合物既包含已知抗菌母核，也涵盖新型抗菌母核。本研究表明，经过充分验证的贝叶斯分类定量构效关系方法，可作为其他筛选手段的补充，用于发掘新型且具有开发潜力的命中化合物。本研究构建与验证模型所用的数据集均已公开共享。