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- W2079494052 endingPage "2028" @default.
- W2079494052 startingPage "2013" @default.
- W2079494052 abstract "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).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.Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents." @default.
- W2079494052 created "2016-06-24" @default.
- W2079494052 creator A5031900984 @default.
- W2079494052 creator A5077424712 @default.
- W2079494052 date "2014-12-01" @default.
- W2079494052 modified "2023-10-02" @default.
- W2079494052 title "Chemoinformatics for medicinal chemistry: <i>in silico</i> model to enable the discovery of potent and safer anti-cocci agents" @default.
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- W2079494052 doi "https://doi.org/10.4155/fmc.14.136" @default.
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