CV

PhD

The University of Akrons

Chemical Engineering

BS

Chongqing University

Bioengineering

Senior Technical Analyst | Computational Chemist

Guidehouse

Senior Technical Analyst at Guidehouse and serving as a Computational Chemist at NIH, leverage different computational technicals for drug discovery.

• Led the design and operationalization of integrated, end-to-end discovery workflows combining docking, MD-based refinement, alchemical free energy perturbation (FEP), and ML-driven developability prediction to guide compound prioritization across NIH-funded programs.
• Owned the architecture and reproducibility standards for alchemical FEP pipelines using OpenMM and MBAR, enabling quantitative protein–ligand binding affinity prediction and decision support for structure-based lead optimization.
• Curated and analyzed target-focused chemical libraries to support iterative design–test–learn cycles, partnering with medicinal chemists to leverage free-energy–informed SAR analysis for hypothesis refinement and compound selection.
• Applied structure-based, ligand-based, and fragment-based design strategies to manage trade-offs among potency, selectivity, and developability constraints.
• Served as a technical bridge between computation and experimental teams, translating FEP trends, MD-derived conformational insights, virtual screening, and machine learning results into actionable hypotheses that directly informed synthesis and experimental prioritization.

Postdoctoral Fellow | Research Fellow

National Cancer Institute, National Institute of Health

• Independently led multiple structure- and mechanism-driven discovery efforts in cancer signaling, driving hypothesis formation, computational strategy, and interpretation across protein–ligand and protein–protein interaction systems.
• Developed reusable computational pipelines integrating MD-based conformational sampling with free-energy analyses (MM/GBSA, FEP) to enable systematic evaluation across multiple targets and ligand series.
• Identified cryptic and allosteric binding pockets via MD-driven conformational exploration, informing inhibitor and PROTAC design strategies.
• Applied AI-based protein structure prediction tools (AlphaFold, RoseTTAFold) to generate working models for proteins and protein–protein complexes in the absence of experimental structures, enabling downstream modeling and interaction analysis.

Research Assistant

The University of Akron

• Built computational chemistry and biology pipelines to model protein–protein, protein–ligand, protein–membrane, and peptide interactions, supporting small-molecule and peptide-based therapeutic design.
• Applied molecular docking and MD simulations to characterize binding mechanisms and conformational behavior, guiding structure- and sequence-level optimization.
• Developed ML/AI models to predict sequence/structure–property relationships for peptides, small molecules, and biofunctional materials, working closely with experimental collaborators for validation.

mTOR variants activation discovers PI3K-like cryptic pocket, expanding allosteric, mutant-selective inhibitor designs

American Chemical Society

Dysregulated mechanistic target of rapamycin (mTOR) signaling is implicated in various human diseases, including cancer and neurological disorders. Here, we investigate the structural dynamics of mTOR variants associated with hyperactivation, revealing a cryptic pocket akin to that in phosphoinositide 3-kinases (PI3Ks). Our findings suggest new avenues for designing allosteric, mutant-selective inhibitors targeting this cryptic site, potentially enhancing therapeutic strategies against mTOR-driven pathologies.