29 structured courses across 6 tracks — built by the same team that runs our service delivery and the journal.
Comprehensive training covering clinical trial design, site management, CRF creation, CTMS tools, and end-to-end trial monitoring. Industry-aligned with ICH E6(R2) GCP standards.
Learn adverse event case processing, signal detection methods, PSUR writing, and global PV regulations. Hands-on practice with real-world safety narratives and VigiBase-style databases.
Understand global regulatory frameworks, CTD dossier preparation, GCP audit readiness, and CDSCO/FDA submission strategies. Includes mock regulatory scenarios and case-based learning.
Master eCRF design, database build, discrepancy management, and database lock procedures. Practical training on Medidata Rave concepts and CDISC standards — the backbone of modern clinical data.
Learn MedDRA and WHODrug coding principles for adverse events and medications. Introduction to SAS programming for clinical trial data analysis, ADAM datasets, and TLF generation.
Master the craft of writing original research articles, review papers, and case reports using IMRaD structure. Learn journal targeting, submission portals, responding to reviewers, and navigating the publication process.
Specialized training in regulatory medical writing — Clinical Study Reports (CSR), Investigator Brochures (IB), informed consent forms, and package inserts. Industry-standard formatting and ICH guidelines covered.
Practical workshop-style training on drafting full manuscripts — from abstract to acknowledgements. Covers Zotero/Mendeley reference management, plagiarism checking, and indexed vs. predatory journal evaluation.
Step-by-step training in conducting systematic reviews using PRISMA 2020 guidelines. Practical sessions on literature screening tools, data extraction, risk of bias assessment, and meta-analysis using RevMan and R.
Understand research design principles — RCTs, cohort, case-control, cross-sectional studies. Applied biostatistics using SPSS and GraphPad Prism: t-tests, ANOVA, regression, and survival analysis.
Foundation course covering sequence alignment, protein structure prediction using AlphaFold, phylogenetic analysis, and major biological databases. Hands-on with Biopython and NCBI tools.
Deep dive into differential gene expression analysis using RNA-seq, DESeq2, and edgeR. Covers SNP/INDEL variant calling, functional annotation, pathway enrichment, and mutation impact prediction.
Computational vaccine design using immunoinformatics tools — B/T cell epitope prediction, allergenicity assessment, and multi-epitope vaccine construction. Applied to infectious diseases and cancer immunotherapy.
Complete NGS pipeline from raw FASTQ to variant reports — quality control, adapter trimming, genome alignment, variant calling with GATK, and functional annotation using ANNOVAR and SnpEff.
Build and analyze compound-target-disease networks using Cytoscape, STRING, and SwissTargetPrediction. Covers hub gene identification, pathway enrichment, and target-disease interaction for herbal and synthetic drugs.
Complete workflow: receptor preparation, ligand preparation, docking using AutoDock Vina and Glide, result interpretation, and 2D/3D visualization using PyMOL and Discovery Studio. Real target-ligand case studies included.
Full MD simulation pipeline using GROMACS — system setup, energy minimization, equilibration, production run, and trajectory analysis. Covers RMSD, RMSF, Rg, SASA, and hydrogen bond analysis for protein-ligand complexes.
Advanced course covering 3D pharmacophore modeling, homology modeling with MODELLER/AlphaFold, fragment-based drug design, and de novo drug design strategies for validated pharmaceutical targets.
Apply machine learning to drug discovery — QSAR modeling, generative molecular design, target prediction, and bioactivity prediction using DeepChem, RDKit, and graph neural network architectures.
In-silico ADMET profiling of drug candidates using SwissADME, pkCSM, and ADMETLab tools. Covers drug-likeness filters, Lipinski rules, BBB permeability, metabolic stability, and toxicity predictions.
Python from scratch for life scientists — data types, functions, libraries (NumPy, Pandas). Applied projects: molecular fingerprinting with RDKit, protein sequence analysis with Biopython, and automated docking result parsing.
R for biological data analysis — statistical testing, data wrangling with tidyverse, publication-quality plots with ggplot2, and bioinformatics workflows using Bioconductor packages for gene expression analysis.
Supervised and unsupervised ML applied to life sciences — classification, regression, clustering for biological datasets. Projects include bioactivity prediction, cancer subtype classification, and protein function prediction.
Create journal-quality visualizations — heatmaps, volcano plots, network graphs, box plots, and survival curves. Covers both Python-based (Matplotlib, Seaborn, Plotly) and GraphPad Prism for non-coders.
Pharmaceutical QMS design, SOP writing, deviation management, CAPA implementation, and change control. Covers ICH Q10, FDA 21 CFR Part 211, and audit preparation for QA roles in pharma industry.
Analytical method validation, raw material testing, in-process and finished product testing. Pharmacopoeia standards (IP, BP, USP), HPLC method development, dissolution testing, and stability study design per ICH Q1A.
GMP fundamentals for oral solid, liquid, and sterile dosage forms. Plant layout and clean room design, process validation protocols, batch record review, environmental monitoring, and WHO/Schedule M compliance.
Preformulation studies, formulation design for tablets, capsules, suspensions, and semi-solids. Design of Experiments (DoE), excipient compatibility, scale-up considerations, and technology transfer documentation.
Product launch strategy, brand management, medical representative training fundamentals. Regulatory documentation for India (CDSCO/DCGI) — marketing authorization applications, dossier assembly, and post-approval changes.