Master AI-Powered Drug Discovery with BioPython: Immuno-Chemoinformatics in 4 weeks through hands-on, project-based online training with DSTC.
Drug Discovery & Pharmaceutical Sciences
Module-by-module breakdown of AI-Powered Drug Discovery with BioPython: Immuno-Chemoinformatics, from foundations to a certified capstone project.
Toolkit
โข BioPython SeqIO and Entrez for programmatic retrieval from NCBI and UniProt
โข Alignment handling and the parsing errors that silently corrupt downstream analysis
โข Reproducible environments and scripted pipelines instead of manual web-tool clicking
Antigens
โข Antigen selection, conservation analysis and surface accessibility
โข MHC class I and II presentation pathways and the constraints on epitopes
โข HLA polymorphism and population coverage as a hard design requirement
Epitopes
โข IEDB tools and NetMHCpan-class predictors, with their training-data bias
โข Immunogenicity versus binding affinity โ predicted binders often do nothing
โข B-cell and conformational epitope prediction and its notably poorer accuracy
Chemistry
โข RDKit for descriptors, fingerprints and similarity searching
โข Similarity-based scaffold hopping and the activity cliffs that defeat it
โข Toxicity and off-target screening before a compound is taken further
Integration
โข Linking epitope output to chemical screening in one scripted pipeline
โข Autoimmunity screening against the human proteome as a safety gate
โข A small end-to-end project written up so a reviewer can rerun it
e-Certificate and e-Marksheet issued on successful completion.