Course Catalog & Credential Pathway — Dwg. No. PF-002A
A field branch of the Construction Track, for students aimed specifically at AI-for-science and drug-discovery research roles — the same architectures, pointed at protein structure instead of language.
Field branch of Dwg. No. PF-002, The Construction Track — same core sequence through Phase II, concentrated here into computational biology & AI drug discovery from Phase II onward. See also Dwg. No. PF-001, The Verification Track.
Five core strands, plus the domain strand this branch concentrates in
No frameworks yet, and no biology yet either. Two years of straight math and programming fundamentals, identical to the core Construction Track — the deficit that shows up latest and hurts most in this field is always a weak math foundation, not an unfamiliar wet-lab term.
Precalculus, Accelerated
Compressed to clear room for calculus by grade 11 — the standard on-ramp for anyone headed toward a quantitative degree.
Programming I — Python
Variables, control flow, functions, data structures. Fluency, not tricks.
Discrete Mathematics & Logic
Sets, proofs, graphs, combinatorics — the math CS theory actually runs on, usually skipped until it's overdue.
Technical Writing for Research
Writing a clear methods section and an honest results section — the two paragraphs every paper lives or dies on.
The math and CS sequences converge into an actual first model, same as the core track. The domain layer begins here too, concurrently — by the end of Phase II a student can both train a small model and read a protein structure paper without translation help.
Calculus I & II
Through multivariable and the gradient — backpropagation is the chain rule with bookkeeping, and it should read that way.
MATH 101
Linear Algebra
Vector spaces, eigendecomposition, matrix calculus. Every tensor operation is this course wearing a framework's syntax.
MATH 101
Probability & Statistics for ML
Distributions, estimation, Bayes — framed toward loss functions and uncertainty, not toward the social-science stats track.
Data Structures & Algorithms
Complexity analysis and the standard structures, drilled to fluency — still the baseline technical-interview bar at every AI lab.
CS 100
Intro to Machine Learning
Regression through a first neural net, built from array operations before any framework is allowed to hide the mechanics.
MATH 210, STAT 220
Research Seminar — Reading Group
Weekly seminal-paper reads (perceptron through transformers), presented and defended aloud, not just summarized.
Organic Chemistry & Biochemistry Foundations
Molecular structure, reaction mechanisms, protein and nucleic-acid chemistry — the vocabulary every course below this one assumes.
MATH 201 (concurrent)
Structural & Molecular Biology
Protein folding, binding, enzyme kinetics — what a docking score or a binding-affinity number actually means, and why a chemically valid molecule can still be biologically useless.
CHEM 215
Here the domain degree takes over from the general CS/applied-math track. A joint computational biology, bioinformatics, or chemistry-plus-CS program replaces DEG 300 — the credential and peer network it buys are specific to this field, not interchangeable with a general CS degree.
Computational Biology / Bioinformatics Degree
Formal core in a joint comp-bio, bioinformatics, or chemistry-plus-CS program — the credential and the peer network both matter here, and both are field-specific.
Computational Biology & Structure Prediction
Sequence-to-structure methods from Rosetta through AlphaFold and RFdiffusion, cheminformatics representations, and molecular-dynamics basics — the applied layer between ML 310's architectures and a result a wet lab would trust.
BIO 225, ML 230
Deep Learning for Structural Biology
Transformer and diffusion architectures in depth, taught directly against the systems that made them famous in this field — AlphaFold's structure module, RFdiffusion's generative backbone design — rather than as a generic survey.
ML 230
Systems for ML
Distributed training, GPU/accelerator programming, the infrastructure that turns a notebook model into one that trains at scale — the same infrastructure a structure-prediction pipeline runs on.
CS 210
Supervised Research Practicum
Placement inside a computational-biology or drug-discovery lab, with a mentor of record. Graded on a reproduction that actually reproduces, or a contribution that gets merged.
RES 240, ML 310
Capstone — Build & Ship
One model or tool — a docking pipeline, a structure-prediction fine-tune, a molecule-screening service — taken from idea to a deployed, load-bearing artifact with real users.
ENG 320, RES 350
No end date, for two compounding reasons: the field's ML baseline moves every conference cycle, and the wet-lab validation baseline moves on its own, slower schedule that a purely computational graduate can lose track of.