MCHM3001 From Molecules to Therapeutics

How MCHM3001 is assessed

What MCHM3001 covers

MCHM3001 From Molecules to Therapeutics runs across three teaching themes — target identification and hit discovery, optimising target interactions, and getting the drug to market — and this thirteen-chapter map follows the lecture schedule through all three.

The unit is assessed by continuous coursework (a lay presentation, a group TGA presentation, an in-silico docking figure, two in-person tests and a fragment-based-drug-design practical project and lab report) plus a comprehensive final exam worth 40% of the unit. Use the map to see how each week's method builds toward that final.

The University of Sydney structures MCHM3001 around three themes — target identification and hit discovery (omics, the druggable proteome, cell models, antibodies, high-throughput and display screening), optimising target interactions (in-silico design, hit-to-lead synthesis, lead optimisation and ADME) and getting the drug to market (economics, clinical trials, imaging agents, toxicology and TGA regulation).

It is a method-and-reasoning subject: the marks live in naming the right stage or model, applying the taught relationship (IC50, the Z-factor, Lipinski's rule of 5, the Tanimoto coefficient, percentage ionisation, exposure ratios), and interpreting the result.

Assessment runs through Canvas as continuous coursework — a 10% lay presentation, a 10% group TGA-approval presentation, a 10% in-silico docking figure, two in-person tests worth 5% each, a 16% fragment-based-drug-design practical project and lab report, and small safety quizzes — capped by a comprehensive final exam worth 40% of the unit.

The unit provides no sample or past exam paper and lists the exam format as 'to be advised', so this guide fills that gap by teaching the examinable method across every stage of the pipeline; the MCHM3001 result also feeds the Weighted Average Mark (WAM) that later units build on.

Worked example · free

IC50 of a fragment-derived lead against MMP-3

Key terms

IC50

The inhibitor concentration that halves a biological or biochemical activity (50% inhibition). It is assay-dependent (relative), quoted in molar units; an optimised drug typically sits in the low-nanomolar range.

Dissociation constant (KD)

For A + B ⇌ AB, KD = [A][B]/[AB] = k_off/k_on, in concentration units. When free ligand equals KD, half the protein is bound; a lower KD means higher affinity.

Lipinski's rule of 5

A developability filter for oral absorption: MW < 500 Da, H-bond donors < 5, H-bond acceptors < 10, and cLogP < 5; an orally active drug generally has no more than one violation.

Druggable proteome

The subset of proteins (~3,000 of the ~20,000 human proteins) that can bind a drug-like small molecule with useful affinity; a drug target must also be disease-linked and disease-modifying.

Pharmacophore

The common three-dimensional arrangement of features (H-bond donor/acceptor, hydrophobe, aromatic ring) shared by active molecules, defined by inter-feature distances and angles — the basis of ligand-based design.

Common Technical Document (CTD)

The five-module dossier a sponsor submits to a regulator such as the TGA: M1 region-specific admin, M2 summaries, M3 quality/CMC, M4 non-clinical reports, M5 clinical reports.

MCHM3001 FAQ

Questions students ask in each MCHM3001 chapter

What is the difference between the druggable genome and the set of drug targets?

The druggable genome is every protein that can physically bind a drug-like small molecule with useful affinity — about 3,000 human proteins. A drug target must additionally be disease-modifying, so the real target universe is the intersection of the druggable set (~3,000) with the disease-gene set (~3,000–10,000), estimated at roughly 600–1,500 proteins. A protein can be very druggable yet useless as a target if hitting it does not change disease. Chapter 01 · Drug Discovery: History & the Modern Pipeline

Why does trypsin not cleave before a proline?

Proline's ring locks the peptide backbone into a geometry that trypsin's active site cannot accommodate for catalysis, so a lysine or arginine immediately followed by proline is skipped. This is a favourite exam point: when you predict tryptic peptides, always check the residue after each K or R and leave a K-P or R-P bond intact. Chapter 02 · Omics-Informed Target Discovery

Why is most of the proteome called 'undruggable'?

Because a classic drug needs a well-defined pocket to bind with useful affinity, and most proteins — especially those acting only through flat protein-protein interfaces — do not offer one. Up to ~85% of the proteome falls into this category. New modalities (PPI inhibitors, PROTAC degraders, pharmacological chaperones and oligonucleotide therapies) are the strategies MCHM3001 teaches for expanding the druggable proteome. Chapter 03 · The Druggable Proteome & Modulating Gene Expression

Why are 3D cultures considered more predictive than 2D monolayers?

A 2D monolayer forces cells into an unnatural flat, polarised sheet on stiff plastic, whereas 3D models restore soluble gradients, three-dimensional cell-cell and cell-matrix adhesions, and tissue-like (kPa, not GPa) stiffness. Organoids grown from iPSCs even self-organise into mini-organ architecture. That closer physiology makes 3D systems better at predicting how a drug will behave in tissue, at the cost of complexity. Chapter 04 · Cell Models & Antibody Discovery

What does the Z-factor actually tell you?

It tells you whether an assay can reliably distinguish active from inactive wells before you commit to a large screen. It compares the combined spread of the positive and negative controls (three standard deviations each) against the gap between their means. Z near 1 means the control clouds are tight and far apart (excellent); Z between 0.5 and 1 is fit for HTS; Z below 0 means the clouds overlap and the assay is useless for screening. Chapter 05 · High-Throughput Screening & Hit Identification

How do you calculate the size of a combinatorial library?

Use products = nˣ, where n is the number of building blocks available at each step and x is the number of steps or positions. For a tripeptide built from 20 amino acids at each of 3 positions, that is 20³ = 8,000 compounds. The exponent is always the number of steps, so adding a fourth position (20⁴ = 160,000) grows the library far faster than adding building blocks. Chapter 06 · Macrocycles & Massively-Parallel Screening

How do you decide between ligand-based and structure-based design?

It comes down to whether you have a 3D structure of the target. With a solved structure (X-ray, cryo-EM, NMR) you can use structure-based methods — dock candidates into the actual binding site and run virtual screening. Without one, you use ligand-based methods: build a pharmacophore from known actives and exploit structure-activity relationships and bioisosteres. Many projects use both as structures become available. Chapter 07 · In-Silico Drug Design

How do you compute a Tanimoto coefficient from two fingerprints?

Count three things: c, the bits set in both molecules; a, the bits set only in the first; and b, the bits set only in the second. Then T = c/(a+b+c) — the intersection over the union. If A and B each have 8 bits set and share 6, then a = b = 2, c = 6, so T = 6/10 = 0.60. The commonest error is using the full bit counts for a and b instead of the bits unique to each. Chapter 08 · Hit-to-Lead: Synthesis & Chemical Space

Why can chasing raw potency (IC50) mislead during optimisation?

Because potency can be increased simply by making a molecule greasier — more lipophilic groups often bind hydrophobic pockets better — but high logP brings poor solubility, faster metabolism, and greater off-target and hERG risk. Lipophilic efficiency (LipE = pIC50 − logP) strips out the contribution of lipophilicity, so it reveals whether an analogue is genuinely binding better or just heavier and greasier. The higher-LipE analogue is usually the better lead even if it is not the most potent. Chapter 09 · Lead Optimisation

What reaction do cytochrome P450s catalyse, and why does it matter?

CYP450s are heme monooxygenases that oxidise otherwise unreactive C-H bonds: R-H + O₂ + 2H⁺ + 2e⁻ → R-OH + H₂O, inserting one oxygen atom into the substrate and reducing the other to water via the highly reactive Compound I intermediate. It matters because this Phase I oxidation is how most drugs are cleared, so where a CYP attacks a molecule sets its half-life and can create active or toxic metabolites. Chapter 10 · ADME & Drug Metabolism

What are the phases of a clinical trial?

Phase 0 is an optional exploratory microdose to check pharmacokinetics. Phase I tests safety, tolerability and dose in a small number (about 20–80) of usually healthy volunteers. Phase II looks for efficacy and more safety in several hundred patients. Phase III is a large confirmatory trial (hundreds to thousands) comparing the drug against standard care to support approval. Phase IV is post-marketing surveillance for long-term safety and new indications. Chapter 11 · Drug Development: Economics, Clinical Trials & Regulation

How do you calculate how much of a radiotracer is left after some time?

Use A = A₀(½)^(t/t½), where A₀ is the starting activity, t½ the half-life and t the elapsed time. For whole-number multiples of the half-life you can just halve repeatedly: a 400 MBq F-18 dose (t½ 110 min) is 200 MBq after 110 min and 100 MBq after 220 min. For other times, compute the exponent t/t½ and raise one-half to that power. Chapter 12 · Radiopharmaceuticals: PET & SPECT Imaging

What does it mean if a compound is Ames-positive only with S9?

The S9 fraction adds mammalian metabolic enzymes (mainly CYP450s) that the test bacteria do not have. If the compound is mutagenic only when S9 is present, then it is not the parent molecule that damages DNA but a reactive metabolite produced by metabolism — a process called bioactivation. That distinction matters, because you may be able to redesign the molecule to block the metabolic step that creates the genotoxin. Chapter 13 · Preclinical Toxicology

How to study for the exam

Treat MCHM3001 as one continuous pipeline rather than 23 separate lectures: for every method, be able to say which stage it belongs to (target ID, hit discovery, optimisation, or market) and why that stage needs it.

Each week, work one problem of that week's quantitative method end to end — IC50 from a % control line, a Z-factor from control means and SDs, a Lipinski/Veber violation count, a Tanimoto coefficient, a percentage-ionisation calculation, an exposure ratio, a radio-isotope half-life decay — writing the relationship, substituting with units, and interpreting the number.

Because Test 1 and Test 2 sample the first and second halves of the semester, keep earlier methods warm rather than filing them away. For the comprehensive 40% final, prioritise breadth: you should be able to start a question anywhere on the pipeline before you deepen the topics you find hardest.

When a step will not click — the CYP catalytic cycle, why only the space-mean of druggability counts, or how deconvolution recovers a hit — ask Sia to re-explain that single step a different way and set you a fresh practice question in the same style; it teaches the method and checks your reasoning, and never substitutes for your own graded work.

Confirm the exam date, room and format on Canvas and the University of Sydney exam timetable.

Your AI Chemistry tutor for MCHM3001

Stuck on a hard MCHM3001 question? Sia is AskSia’s AI Chemistry tutor — ask any MCHM3001 From Molecules to Therapeutics question and get a clear, step-by-step explanation grounded in how the course is actually taught and assessed. Read this whole study guide free, then take your hardest questions to Sia.