Lexin Chen

About

Welcome!

I am a Chemistry Ph.D. student at the University of Florida in the Miranda-Quintana Group with a focus on computational chemistry and data science. My main research interest is developing softwares for clustering, data analysis of Molecular simulations, and charge-transfer studies.

Outside of research, I am devoted to outreach toward diversity in STEM and stimulating scientific curiosity. I am also an artist/animator of biomolecules, see portfolio below. I enjoy photography, pilates, investing, and catching up on geopolitics in my free time.

Selected Software Contributions

MDANCE

Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) is a flexible n-ary clustering package that provides tools for clustering Molecular Dynamics trajectories. The package is designed to be modular and extensible, allowing for the addition of new clustering algorithms and similarity metrics. MDANCE transcends the limitations of traditional approaches by introducing novel, linear scaling (highly efficient) clustering algorithms and can be highly useful in applications such as drug design, million-molecule molecular docking, and molecular dynamics analysis.

Technologies used: Python

PRIME

Refining protein structures is important because the accuracy of protein structures influences our understanding of their function and their interactions with other molecules, which can help design new drugs to target specific molecular interactions. Six different ways of determining the native structure of biomolecules were introduced. Protein Retrieval via Integrative Ensembles (PRIME), consisting of tools to determine the native structure using the extended continuous similarity. PRIME was validated to several replica-exchanged systems, flexible protein, and protein-peptide systems to identify the ensemble representative. PRIME perfectly mapped all the structural motifs in the studied systems and required unprecedented linear scaling. Easy integration to clustering or simulation data.

Technologies used: Python

Quantifying Specific Ions Effect

The mystery of ion selectivity in electrolyte solutions has puzzled scientists for centuries with little known why nature is keen on selecting one ion over the other, known as the specific ions effect. This project allows users to predict specific ion effect (SIE) properties through magnitudes extracted from conceptual DFT to approximate the charge and radius of an ion with or without perturbed descriptors and using different models of charge transfer in solution.

Technologies used: Python

MD Starters

md-starters provides sample code for preparing and analyzing molecular dynamics simulations, mainly for membrane proteins.

Technologies used: Python

Technical Skills

Developed data analysis pipelines in Python, resulting in a 25x increase in efficiency.

Independently managed 16 Git repositories, ensuring proper documentation and version control.

Fluent in Blender with experience creating high-quality scientific renders for publications and presentations for molecules and biomolecules.

Conducted molecular dynamics simulations to investigate tricky systems such as protein-ligand interactions and membrane proteins using AMBER and NAMD.