As a Research Scientist with a deep expertise in chemoinformatics and intelligent chemical design, I focus on advancing the field through innovative research in small molecule reactivity and cheminformatics methods for AI models. With a solid foundation in medical and medicinal chemistry, I've contributed to diverse projects ranging from molecular co-crystals research to receptor/ligand interactions analysis, utilizing advanced computational tools and algorithms.
For a detailed overview of my education, work experience, research activities, personal qualities, and skills, please check my resume.
Projects
This project automates the design of potentially immunogenic peptides from protein structures. The script identifies cleavage sites, generates candidate peptides, optimizes their immunogenicity, and selects the best ones for display.
Stack: Python, AlphaFold2, MDTraj, NumPy, BioPython, Skikit-Learn
TThis project offers computational chemistry interns tasks like molecule parameterization with OpenFF for Gromacs and ligand preparation for Autodock Vina docking. Focused on tool automation, it assesses interns' coding and documentation skills without requiring full program development.
Stack: Python, RDKit, openff-toolkit, Meeko
This project delves into drug discovery for Type 2 Diabetes Mellitus, from dataset analysis of drug candidates to validation through experimental and computational methods. It highlights Python's role in computational chemistry, encompassing data analysis, hypothesis testing, and simulations.
Stack: Python, Pandas, Seaborn, Matplotlib, Biopython, GROMACS, PyMOL, py3Dmol
This script is designed to extract antibody amino acid chain sequences from PDB files. The SAbDab database is used to process the files. It allows for extraction of amino acid sequences, deletion of empty files, and clustering of sequences using different algorithms.
Stack: Python, Anaconda, DBSCAN, K-means, Hierarchical Clustering, Biopython, Scikit-learn, Numpy, Matplotlib, Scipy, Seaborn
Our team's research, centered on small molecule reactivity, merges compound database development with graph convolutional networks to predict reactivity via Fukui indices and CDD. Leveraging tools like MOPAC, ORCA, and Chemprop within a Python and PyTorch framework, we conduct analyses on the Lomonosov-2 cluster, employing SLURM and bash for efficiency.
Stack: Python, PyTorch, Chemprop, MOPAC, ORCA, SLURM, Bash