Date of Award


Degree Name

Doctor of Philosophy




Lela L. VukoviÄ?


Binding affinity between two molecules is an essential property in drug and sensor discovery. Several computational and experimental methods exist to find molecules with high binding affinities to desired target molecules. These methods are often complementary, where fast computational methods can be used for the initial screening of molecules, and experimental methods can then screen and determine the molecules of interest and sometimes define the structures of bound complexes. After these steps, computational methods, like molecular dynamics (MD) simulations, can provide detailed insights into atomic interactions and binding, and machine learning approaches can analyze experiment-derived data to discern patterns and trends. The above computational methods were employed to tackle several research questions in this dissertation. In the first project, lipid-wrapped single-walled carbon nanotube (SWNT) conjugates and their interactions were examined with several membrane-disrupting molecules. The results of our simulations with the experimental optical emission spectra of these conjugates were compared, and the magnitude of the optical signal from the magnitude of the observed structural disruption was predicted. In the subsequent project, machine learning approaches were used to predict new DNA sequences in DNA-SWNT conjugates that can sense serotonin molecules. In the last project, BinderSpace, an open-source Python package was coded and developed for motif analysis, sequence visualization, and clustering. This tool was instrumental in analyzing datasets of oligonucleotides binding to single-wall carbon nanotubes and cyclic peptidomimetics interacting with bovine carbonic anhydrase protein. Overall, this dissertation demonstrates the effective combination of computational methodologies in molecular science and contributes valuable tools and knowledge that can significantly impact sensor technology.




Recieved from ProQuest

File Size

104 p.

File Format


Rights Holder

Payam Kelich