Md. Asif Uddin

    Chapter 10 VII.10

    Molecular Graphs and Drug Discovery

    A molecule is a graph over atoms, and how the data is split decides whether any reported score generalises.

    A molecule is a graph, and the split decides whether the score means anything.

    How this chapter is built

    M2Substantive

    The derivations are the chapter. A reader who skips the algebra has not learned it.

    basics2/9what the words mean
    concept2/2what to picture
    theory0/2why it works, and when it does not
    mathematics0/9derive it, then compute it
    practice0/6build it, break it, read the papers

    Five strands, not one. Mathematics is the spine; the other four are the body. A chapter cannot pay its way out of teaching with problems, nor out of problems with teaching.

    Before you start

    The problem

    Chemistry hands biology a graph with no coordinates and clear semantics. What makes screening hard is not the representation but the evaluation: a random split leaks scaffolds and reports a number that will not survive contact with a new series.

    Dataset fingerprint to pipelineFive measured properties of a dataset enter a set of heuristic rules, which resolve them jointly under a GPU memory budget into five pipeline decisions. The architecture is not among the things being chosen.fingerprintpipelinevoxel spacingsimage sizesintensity distributionmodalityclass ratiosheuristic rulesunder a memory budgettarget spacingresamplingnormalisationpatch and batch sizedepth and poolingFixed regardless: Dice plus cross-entropy, SGD at 0.99, 1000 epochs, the same augmentations every time.The original paper used no residual connections, no attention, no squeeze-and-excitation. That was the claim.
    Fig. 10 — A dataset fingerprint resolved by heuristic rules into a whole pipeline under a memory budget. The architecture is not among the things being chosen.

    What this chapter covers

    • Molecular representation
    • Protein structure
    • Drug discovery
    • Disease modelling
    • Biomarker discovery

    Apparatus

    The mathematics this chapter leans on, held in Book 0 so it can be assumed here without being taught here. Not a gate — follow a link when a step stops making sense.

    Graphs, vertices and edges 0.GR.01 · Walks, paths, cycles and connectivity 0.GR.03 · Inner products, norms and cosine similarity 0.LA.03 · Estimators, bias and variance 0.ST.01

    Notation

    • dModel width
    • 𝔼Expectation
    • θ̂An estimate, as against the quantity it estimates

    Propositions

    Not yet written. The topics above are the plan for this chapter; each will become a proposition with its own figure.

    Worked problems

    0/3 problems0/3 variants0/6 exercisesowes 9 more

    Not yet written. At M2 this chapter owes 3 worked problems across 3 distinct variants, and 6 exercises, every one with a published solution. The build enforces that from the day the chapter is marked published.