CHEMINFORMATICS · AI · MOLECULAR DESIGN
Computational chemistry for better drug discovery.
I develop cheminformatics workflows, machine-learning models, and accessible scientific software for molecular design, virtual screening, and ADMET prediction.
What I work on
My work connects chemistry, data science, and software engineering to make computational methods more useful in real research settings.
Cheminformatics
RDKit-based molecular representations, chemical databases, similarity searching, and reproducible data workflows.
AI for drug discovery
QSAR, deep learning, ADMET prediction, and AI-assisted approaches to explore chemical space.
Molecular modeling
Molecular docking, virtual screening, molecular dynamics, and structure-based drug design.
Featured work
WEB APPLICATIONS
Chemoinformatics toolkit
A collection of browser-based tools for molecular analysis, ADME testing, drug-likeness filters, and chemical data exploration.
Open the toolkit →EDUCATION & OPEN SCIENCE
Chemoinformatics tutorials
Practical notebooks and examples for learning cheminformatics, machine learning, and molecular modeling.
Browse the tutorials →RESEARCH SOFTWARE
Open-source experiments
Explore my GitHub work across RDKit, Streamlit, Django, docking, QSAR, and AI-assisted drug discovery.
View GitHub →Latest writing
Let’s collaborate
I’m interested in research and software projects at the intersection of computational chemistry, machine learning, and drug discovery.