MS in Bioinformatics & Bachelor's in Pharmacy
Bioinformatics researcher specializing in drug discovery, single-cell sequencing, and computational biology. Skilled in bioinformatics software development, research automation, and cloud computing. Proficient in Python, R, and high-throughput data analysis for advancing medical research.
Developed a bioinformatics platform to streamline molecular docking with Git-like version control and live molecular editing. Automated predictions using AWS and Google Cloud, enhancing scalability and accuracy using Autodock. Built with Next.js, Node.js, Go, and Docker, integrating SQL and MongoDB for efficient data management.
Awarded a 120,000 INR SSIP Hub grant to lead a team in designing, synthesizing, and evaluating anticancer drug candidates. Utilized molecular dynamics tools like Autodock and RDKit to predict receptor affinity of novel therapeutics.
Designed a distributed system using Google Cloud's Pub/Sub, App Run, and Next.js, alongside Node.js and Python Flask, featuring multiple endpoints for complex pharmaceutical workflows.
Exploring evolutionary changes at the molecular level with an emphasis on genetic and protein modifications, including practical bioinformatics applications in phylogenetics and molecular clock theory.
An introduction to computational drug discovery techniques, covering key methods like molecular docking, QSAR, and ligand-based virtual screening. Ideal for bioinformaticians entering pharmacoinformatics.
A deep dive into the role of scRNA-seq in cancer studies, focusing on its applications for tumor heterogeneity and precision medicine.
Overview of efficient methods for acquiring datasets in bioinformatics, including resources like NCBI, GenBank, and EMBL, with a focus on open-source data for large-scale analyses.
Explains the EADock algorithm and its role in docking small molecules into protein active sites using multiobjective evolutionary optimization, highlighting its utility in structure-based drug design.
A guide to using the TeachOpenCADD library for computational drug design in Python, covering core techniques like molecular modeling, docking, and cheminformatics applications.