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Biomolecules 2018, 8(2), 24; https://doi.org/10.3390/biom8020024

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s

1
Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA
2
Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 19 February 2018 / Revised: 26 April 2018 / Accepted: 27 April 2018 / Published: 7 May 2018
(This article belongs to the Special Issue Machine Learning for Molecular Modelling in Drug Design)
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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. View Full-Text
Keywords: human complement factor C1; virtual high-throughput screening; data-mining; quantitative structure-activity relationship; drug discovery; Signature human complement factor C1; virtual high-throughput screening; data-mining; quantitative structure-activity relationship; drug discovery; Signature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, J.J.; Schmucker, L.N.; Visco, D.P., Jr. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s. Biomolecules 2018, 8, 24.

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