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• W2800763908 startingPage "24" @default.
• W2800763908 abstract "When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue damage occurs. Current Food and Drug Administration approved treatments include supplemental recombinant C1 inhibitor, but these are extremely costly and a more economical solution is desired. In our work, we have utilized an existing data set of 136 compounds that have been previously tested for activity against C1. Using these compounds and the activity data, we have created models using principal component analysis, genetic algorithm, and support vector machine approaches to characterize activity. The models were then utilized to virtually screen the 72 million compound PubChem repository. This first round of virtual high-throughput screening identified many economical and promising inhibitor candidates, a subset of which was tested to validate their biological activity. These results were used to retrain the models and rescreen PubChem in a second round vHTS. Hit rates for the first round vHTS were 57%, while hit rates for the second round vHTS were 50%. Additional structure–property analysis was performed on the active and inactive compounds to identify interesting scaffolds for further investigation." @default.
• W2800763908 created "2018-05-17" @default.
• W2800763908 creator A5010434742 @default.
• W2800763908 creator A5020668440 @default.
• W2800763908 creator A5047605536 @default.
• W2800763908 date "2018-05-07" @default.
• W2800763908 modified "2023-09-27" @default.
• W2800763908 title "Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s" @default.
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• W2800763908 doi "https://doi.org/10.3390/biom8020024" @default.
• W2800763908 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6023033" @default.
• W2800763908 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29735903" @default.
• W2800763908 hasPublicationYear "2018" @default.
• W2800763908 type Work @default.

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