Python for Bioinformatics
Go from your first script to a working sequence-analysis pipeline in Python — reading FASTA and FASTQ, working with Biopython, and reproducing a result end to end.
Course detailsWhat this course is
Computational drug discovery is often presented as a single clever model that finds a molecule. In practice it is a chain: a validated target, trustworthy data, a model that is evaluated the way it will be used, and a result a chemist can act on. This course walks that chain end to end.
You will work with public data throughout — Protein Data Bank structures for targets and public bioactivity sets for compounds — so every claim in the course can be checked against the primary source rather than taken on trust.
What it is not
This course does not promise that you will invent a drug in eight weeks, and it does not teach molecular dynamics from scratch. It teaches you to use machine learning responsibly inside a discovery pipeline: what the data can support, what a model can honestly claim, and where a result still has to be confirmed at the bench.
How you are assessed
Weekly applied tasks build into a capstone: a short written report on one compound series against one target, including your data provenance, your split strategy, your evaluation metrics and a clearly stated list of limitations. Submissions are reviewed for scientific reasoning, not for a lucky metric.
Syllabus
Weeks 1–2
Targets and structure
How targets are identified and validated, how to read a PDB entry, and what AlphaFold-predicted structures do and do not tell you.
Weeks 3–4
Chemical data
Molecular representations (SMILES, fingerprints, graphs), public bioactivity data, and cleaning a dataset until it is usable.
Weeks 5–6
Models that predict binding
QSAR and deep-learning approaches, scaffolds splits, applicability domains, and metrics that survive scrutiny.
Weeks 7–8
ADMET and a capstone
Absorption, distribution, metabolism, excretion and toxicity prediction, then a written mini-project that defends one compound series.
01/Keep going
Molecular Biology Masterclass
A rigorous pass through the central dogma — replication, transcription, translation, gene regulation and the modern assays used to measure each of them.
Course detailsResearch hub
Method explainers that pair with this course: how the calculations work, and where their assumptions stop holding.
Read the hub