Research

Graduate Research Assistant

I started my Master of Science in Computer at Texas A&M University, College Station in June 2021. I entered into the graduate program as a Graduate Research Assistant and began working with Dr. Thomas Ioerger in the Department of Computer Science and Engineering. At first, I started with a project which involved finding the efficacy of the combination of two drugs in curing Tuberculosis. These drugs were Rifampicin, Bedaquiline, Levofloxacin, and Pyrazinamide. Later, I was moved to a different project which later I wrote thesis on. The main objective of the project was to address the issue of bias in scoring functions in virtual screening.

Here is an abstract to my thesis.
Docking is a computational procedure designed to find molecules that bind possibly the “best” on the protein surface and predict their “correct” bound association. Most docking programs explore the conformation space of ligand by manipulating the position in the protein active site, and the conformation of the ligand by the rotational bonds. To determine the quality of the fit and select the best ligands, most docking programs use an energy-based scoring function. Scoring functions may have a bias that gives preference to certain types of compounds over others. This paper addresses the issue of bias in scoring functions in virtual screening. To mitigate this issue, in our preliminary experiments, we show that a surprisingly accurate model can be developed for predicting docking scores based solely on the molecular properties of the ligands, which explains why there is such a high degree of correlation of scores for compounds between different targets. Our goal is to show how to use this model to “subtract” this bias out, producing a modified score that better shows that compounds dock best, and most specifically to which targets. We then show the performance of the model can be improved by extending the first set of molecular properties calculates for each compound with additional chemical features called “fingerprints.” In this thesis, we use the AutoDock docking program and explore the use of several statistical and machine learning methods to extract and characterize the biases in the VINA score.

I spent my first year as a Graduate Research Assistant working on this project. In the second half of my graduate years, I worked as a Graduate Teaching Assistant. I worked on the project throughout my graduate years even while I was a teaching assistant. I successfully submitted my thesis and it got approved in April, 2023. In May 2023, I graduated from Texas A&M University, College Station with an MS in Computer Science degree.

Undergraduate Research Assistant

I was always enthusiast about research. Even though I had a background in Computer Science, I wanted to work in the Biotech field. Dr. Beatrice Clack(deceased) gave me an opportunity to work on a project which was very dear to her. She specialized in working with a bug called Eurugaster integriceps that infests on the crops. My main objective on the project were:

to perform de novo assembly of sequence reads and profiled the transcriptome of three developmental stages (child, teen, adult) of Eurygaster integriceps using Python
to determine proteins of unique and similar sequences among three stages of Eurygaster integriceps using Blast
I worked with her from August 2019 to May 2020. I only left the project after its completion in May 2020.

Research Intern

In the summer of 2019, I got an opportunity to do internship at Washington University, St. Louis, a prestigious research-focused university with Dr. Christopher Gill in the Department of Computer Science and Engineering. My main responsibilities were:

to develop concurrency platform software in C and C++ through collaboration with team members
to conduct experiments on elastic parallel real-time scheduling and real-time hybrid simulation in cyber-physical systems Later in 2020, a research paper was published. You can find the link to the article in the Publication section.