Courses

4-day hands-on course in CADD, which covers docking using Autodock Vina, drug screening, and drug parameter analysis

Introductory course for biologists who are not familiar with coding. We start from scratch, which will help them in NGS and structural BI analysis

The course mainly focuses on fundamentals of QSAR modelling and predictive bioactivity analysis using Python-based tools.

Master network pharmacology frameworks to systematically map complex "multi-compound, multi-target" interactions in drug discovery. Learn to construct disease-target networks, perform topological analysis, and uncover multi-target mechanism-of-action pathways using Cytoscape and bioinformatic databases.

Master prokaryotic whole genome sequencing (WGS) data analysis from raw reads to functional annotation and phylogenomics. Learn hands-on bioinformatic workflows to assemble, annotate, and compare bacterial genomes with confidence.

The course aims to analyze DNA sequence data generated from metagenomic studies.

Master modern computational pipelines to accurately predict 3D protein structures from amino acid sequences using AI-driven tools like AlphaFold and ESMFold. Learn rigorous validation techniques using Ramachandran plots, ERRAT, and ProSA to ensure structural reliability for downstream modeling.

Master transcriptomic data analysis using the user-friendly, code-free Galaxy platform to process raw RNA-Seq reads into biological insights. Learn end-to-end workflows from quality control and alignment to differential gene expression analysis and pathway enrichment.

Master reverse vaccinology and immunoinformatics pipelines to design multi-epitope vaccine candidates against target pathogens. Learn to predict T-cell and B-cell epitopes, assess allergenicity and toxicity, and perform molecular docking and dynamics to validate vaccine efficacy.

Master DNA sequencing data analysis using the user-friendly, code-free Galaxy platform to process raw genomic reads with confidence. Learn complete bioinformatic workflows from quality control and reference alignment to variant calling and functional annotation.

Master essential Python programming and the Biopython library to automate genomic and proteomic data processing with ease. Learn to parse sequence formats, query biological databases like NCBI, and build custom bioinformatics scripts for real-world research.

Master 3D pharmacophore modeling to identify critical chemical features necessary for optimal ligand-receptor interactions. Learn feature mapping, virtual screening, and structure- and ligand-based strategies to accelerate computer-aided drug discovery.

Master R programming tailored specifically for biological data analysis, visualization, and statistical modeling. Learn to wrangle complex datasets, perform statistical tests, and generate publication-ready plots using ggplot2 and Bioconductor tools.

Master bulk RNA-Seq workflows to transition smoothly from raw sequencing reads to key differential gene expression insights. Learn end-to-end bioinformatic pipelines covering quality control, read alignment, transcript quantification, and pathway enrichment analysis.

From raw file preprocessing to biological pathway enrichment, gain practical hands-on skills to transform complex proteomics datasets into publishable insights.

Master molecular dynamics (MD) simulations to analyze the conformational flexibility, stability, and time-dependent interactions of biomolecular systems. Learn to set up force fields, run production MD trajectories using tools like GROMACS, and perform key post-simulation analyses like RMSD, RMSF, and binding free energy calculations.

Master machine learning and deep learning applications in computational biology to accelerate biological discovery and data interpretation. Learn to leverage AI algorithms for genomic sequence classification, protein structure prediction, drug-target interaction modeling, and biomarker discovery.

Master molecular dynamics (MD) simulations to analyze the conformational flexibility, stability, and time-dependent interactions of biomolecular systems. Learn to set up force fields, run production MD trajectories using tools like GROMACS, and perform key post-simulation analyses like RMSD, RMSF, and binding free energy calculations.