College

College of Engineering (COE)

Date of Last Revision

2020-05-18 08:10:30

Major

Chemical Engineering - Cooperative Education

Honors Course

4200 497-001

Number of Credits

2

Degree Name

Bachelor of Science

Date of Expected Graduation

Spring 2020

Abstract

A large study was conducted to identify possible drug candidates to treat various diseases using computer algorithms, virtual high-throughput screens, and experimental validation of activity. Since the algorithm has the capability to screen millions of compounds, it is beneficial to pharmaceutical development as it allows a larger pool of compounds to be considered that would have otherwise been overlooked. As part of this larger study, this project attempts to identify drug candidates to treat human complement factor C1, a protein which causes tissue damage when underregulated1. A series of designed experiments validate candidates and confirm the performance of the algorithm. After the first round of experiments, the compounds identified through the virtual high-throughput screening had a 57% hit rate of potential compounds and the second round after re-training the models was a 50% hit rate1. By analyzing results from the experiments, potential drug candidates targeting complement factor C1 were identified for additional study. Furthermore, structural analysis of the identified candidates can pinpoint certain features of the compounds resulting in potential leads for further investigation.

1 Chen, J. J., Schmucker, L. N., & Visco, D. P. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s. Biomolecules, 8(2), 24. 7 May 2018, https://doi.org/10.3390/biom8020024

Research Sponsor

Dr. Donald P. Visco, Jr.

First Reader

Dr. Bi-min Newby

Second Reader

Jon Chen

Honors Faculty Advisor

Dr. Bi-min Newby

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