Machine Learning & AI
- Random Forest, SVM
- Predictive QSAR Modeling
- Clinical Outcome Prediction
- Network Pharmacology
Hello, I'm
Vinod Jangid
Computational Biologist
CSIR-NET qualified researcher combining AI-driven drug discovery, structural biology, and bioinformatics. I turn multi-omics data into actionable insights for biopharmaceutical R&D through molecular modeling, virtual screening, and reproducible pipeline automation.
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Years Exp.
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Publications
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Molecules Screened
About Me
Who I amCSIR-NET Qualified Computational Biologist
I specialize in AI-driven drug discovery, structural biology, and multi-omics analysis. My experience spans protein-ligand docking, free energy calculations, homology modeling, RNA-Seq analysis, TCGA-based clinical outcome prediction, and network pharmacology. I build reproducible, containerized pipelines in Python, Bash, Docker, and Snakemake to accelerate analysis and minimize manual effort.
Technologies I work with
Comp. Chemistry & Structural Biology
- HTVS, Hit-to-Lead Optimization
- Protein-Ligand Docking
- Free Energy Calculations
- Homology Modeling
Bioinformatics & Multi-Omics
- RNA-Seq Pipelines (QC, Alignment, DE)
- TCGA Data Analysis
- GO & KEGG Enrichment
- Multi-Omics Interpretation
Tools & Infrastructure
- Python, Bash, Linux/Unix
- GROMACS, AMBER
- AlphaFold, Rosetta, AutoDock
- Schrödinger, PyMOL, Docker
Qualification
Experience & EducationProfessional Experience
Computational Immunologist
Katamaran Industries Pvt. Ltd.
Spearhead the computational engineering of T-Cell Engagers using structural modeling and protein interaction analysis. Support oncology drug repositioning by integrating molecular modeling and multi-omics data.
Bioinformatics Researcher
bioinfoLink
Engineered a fully reproducible, containerized RNA-Seq pipeline using Snakemake, reducing analyst hands-on time by 80%. Applied Random Forest and SVM models to TCGA multi-omics data for clinical outcome prediction.
Bioinformatician & Research Associate
Growdea Technologies Pvt. Ltd.
Deployed machine learning for molecular interaction prediction, developed QSAR models and automated GROMACS MD workflows, and executed HTVS against a novel viral target.
Project Intern
Indian Institute of Science (IISc), Bengaluru
Designed and executed RNA-Seq workflows covering QC, alignment, and differential expression analysis, followed by GO and KEGG pathway enrichment.
Education
M.Sc. Bioinformatics
Bharathiar University, Coimbatore
Thesis: In-silico approach to identify novel drug targets against stress granules for cancer treatment.
B.Sc. Biotechnology
Mohanlal Sukhadia University, Udaipur
Undergraduate training in biotechnology and molecular life sciences.
Awards & Certifications
CSIR-UGC NET (Life Sciences)
All India Rank 46
Genomic Data Science
Johns Hopkins University
Student Project Scheme Fellowship
Tamil Nadu State Council for Science & Technology
Computer-Aided Drug Design
IIT Madras
Human Molecular Genetics
IIT Kanpur
Publications
Research & ContributionsComputational analysis of Non-synonymous SNP effects on human PLVAP gene structure and function
Journal of Applied Genetics, 2025
Machine Learning Optimization Approach to Design Multi-Epitope Marburg Vaccine Construct
Biosciences Biotechnology Research Asia, 2024; 21(4)
Computational Analysis to Identify Novel Drug Targets for Esophageal Cancer
Medinformatics, 2026; 3 (Scopus Q2)
Projects
Selected work highlights
Oncology Drug Repositioning
Integrated molecular modeling and multi-omics data to support drug repositioning strategies and prioritize new therapeutic indications.
High-Throughput Virtual Screening
Executed a screening campaign of 10,000+ small molecules against a viral target using AutoDock, then prioritized the top candidates for validation.
QSAR & Lead Optimization
Developed predictive QSAR models and machine learning workflows to support hit-to-lead optimization and rank promising compounds.
Automated MD Workflows
Built automated GROMACS molecular dynamics workflows to reduce setup time and analyze binding energetics and thermodynamic stability.