Intro
I am passionate about drug discovery and the development of therapeutics that can improve patients' lives.
My academic journey has taken me through chemistry, biology, and computational research, which has given me a multidisciplinary perspective on the drug discovery process. I am particularly interested in computational approaches such as molecular modeling, virtual screening, and AI-assisted drug discovery. During my research experiences, I became increasingly interested in small-molecule therapeutics because of their potential to make treatments more accessible to a broader patient population. I believe that effective medicines should not only work scientifically but should also be accessible to the people who need them. My goal is to contribute to drug discovery research that bridges scientific innovation and real-world patient impact.
Projects
ACE2 allosteric inhibitor
IFD Docking · MD Simulation
2024-2025
PAK4 - CDK2 interaction
Protein - Protein docking
2024
Virtual Screening
2024 - (on going)
AI modeling
AI driven drug development
2026
Review paper
AI Early drug discovery
2023
Experience
Education
License & Certification
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overview
This project investigated the molecular mechanism of ACE2 allosteric inhibitors using induced-fit docking and molecular dynamics simulations. The study aimed to identify binding modes and conformational changes that could disrupt ACE2–RBD interactions without affecting ACE2 enzymatic activity.
Research Question
Can small molecules bind to ACE2 allosteric sites and modulate ACE2–RBD interactions involved in SARS-CoV-2 entry?
Methods
• Induced-Fit Docking (Schrödinger)
• Molecular Dynamics Simulation (Desmond)
• RMSD Analysis
• Protein–Ligand Interaction Analysis
• Allosteric Site Investigation
Key Findings
• Identified a plausible binding mode at ACE2 allosteric site 3
• Observed dimerization loop fluctuations during MD simulations
• Proposed a mechanism for ACE2–RBD disruption
• Poster presentation awarded Best Poster Award
Discovery of Phosphorylation-State Selective Inhibitor for Protein A
Computational prioritization of selective inhibitors targeting the phosphorylated state of Protein A
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Figure 1. Computational workflow for identifying phosphorylation-state selective inhibitors of Protein A
Figure 2. Strategy for phosphorylation-state selective inhibition of Protein A
There is no major binding-site conformational changes -> Targeting the phosphorylation region
overview
This project aimed to identify small molecules that selectively inhibit the phosphorylated state of Protein A while sparing its unmodified form. Because no significant binding-site conformational changes were observed upon phosphorylation, we hypothesized that compounds interacting near the phosphorylation site could achieve state-selective inhibition. A large-scale virtual screening pipeline integrating cheminformatics, MD simulations, ensemble docking, interaction analysis, and ADMET assessment was developed to prioritize compounds for experimental validation.
Research Question
Can small molecules selectively target the phosphorylated state of Protein A?
How can phosphorylation-state selectivity be achieved when phosphorylation does not induce significant binding-site conformational changes?
Can a computational screening workflow efficiently prioritize experimentally testable compounds from a large chemical library?
Methods
Physicochemical Property Filtering
Chemical Liability Filtering
Chemical Diversity Filtering (Morgan fingerprints, similarity-based clustering)
Molecular Dynamics Simulations (Desmond)
Ensemble Docking Using Multiple Protein Conformations
Interaction-Based Prioritization
ADMET Assessment
Synthetic Feasibility and Commercial Availability Assessment
Key Findings
Developed a computational workflow to identify phosphorylation-state selective inhibitors.
Reduced an initial library of 581,130 compounds to 105 experimentally testable candidates through multi-step prioritization.
Established an interaction-based screening strategy using ensemble docking and key interaction fingerprints.
Prioritized compounds for biological evaluation, with experimental validation currently ongoing.