Drug Discovery Informatics Lab, QRC‑Qasemi Research Center, Al‑Qasemi Academic College, Baka EL‑Garbiah 30100, Israel.
Institute of Applied Research‑Galilee Society, Shefa‑Amr 20200, Israel.
Mol Med Rep. 2018 Jul;18(1):763-770. doi: 10.3892/mmr.2018.9027. Epub 2018 May 16.
The aim of the present study was to index natural products in order to facilitate the discovery of less expensive antibacterial therapeutic drugs. Thus, for modeling purposes, the present study utilized a set of 628 antibacterial drugs, representing the active domain, and 2,892 natural products, representing the inactive domain. In addition, using the iterative stochastic elimination algorithm, 36 unique filters were identified, which were then used to construct a highly discriminative and robust model tailored to index natural products for their antibacterial bioactivity. The area attained under the curve was 0.957, indicating a highly discriminative and robust prediction model. Utilizing the proposed model to virtually screen a mixed set of active and inactive substances enabled the present study to capture 72% of the antibacterial drugs in the top 1% of the sample, yielding an enrichment factor of 72. In total, 10 natural products that scored highly as antibacterial drug candidates with the proposed indexing model were reported. PubMed searches revealed that 2 molecules out of the 10 (caffeine and ricinine) have been tested and identified as showing antibacterial activity. The other 8 phytochemicals await experimental evaluation. Due to the efficiency and rapidity of the proposed prediction model, it could be applied to the virtual screening of large chemical databases to facilitate the drug discovery and development processes for antibacterial drug candidates.
本研究的目的是对天然产物进行分类，以便于发现更廉价的抗菌治疗药物。因此，在建模过程中，本研究利用了一组 628 种具有抗菌活性的药物，代表了活性域，以及 2892 种天然产物，代表了非活性域。此外，利用迭代随机消除算法，确定了 36 个独特的过滤器，然后利用这些过滤器构建了一个高度区分和稳健的模型，用于对天然产物进行抗菌生物活性分类。曲线下面积为 0.957，表明该模型具有高度的区分能力和稳健性。利用所提出的模型对活性和非活性物质的混合样本进行虚拟筛选，本研究能够捕获前 1%样本中 72%的抗菌药物，富集因子为 72。总共报告了 10 种评分较高的天然产物，它们是具有抗菌活性的候选药物。PubMed 搜索显示，所提出的索引模型中测试并确定具有抗菌活性的分子有 2 种（咖啡因和奎宁）。其他 8 种植物化学物质有待实验评估。由于所提出的预测模型具有高效快速的特点，因此可以应用于大型化学数据库的虚拟筛选，以促进抗菌候选药物的药物发现和开发过程。
