药物化学中的化学信息学：用于发现高效且更安全的抗球菌药物的计算机模拟模型。
Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents.
作者信息
Speck-Planche Alejandro, Cordeiro Maria Natália Dias Soeiro
机构信息
REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
出版信息
Future Med Chem. 2014;6(18):2013-28. doi: 10.4155/fmc.14.136.
BACKGROUND
Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET).
RESULTS
A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted.
CONCLUSION
Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.
背景
革兰氏阳性球菌是导致严重疾病的耐药性日益增强的细菌。化学信息学有助于合理发现更有效、更安全的抗菌药物。我们开发了一种化学信息学模型，用于同时预测抗球菌活性以及涉及吸收、分布、代谢、排泄和毒性（ADMET）的特征。
结果
创建了一个数据集，其中包含在不同实验条件下测定的来自许多不同化学品的48874个案例。最佳模型在训练集和预测（测试）集中的准确率均约为93%。计算并分析了几个片段对生物学效应的定量贡献。正确预测了研究药物JNJ-Q2的多种生物学效应。
结论
我们的化学信息学模型可作为虚拟筛选有前景的抗球菌药物的有力工具。
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