MCHM3001 · From Molecules to Therapeutics
High-Throughput Screening & Hit Identification
Lecture 9 of MCHM3001 is the quantitative heart of hit discovery: the affinity and potency measures (KD, IC50), the assay-quality metric that decides whether a screen is trustworthy (the Z-factor), the developability filter applied to hits (Lipinski's rule of 5), and the interference compounds to reject (PAINS). These calculations are prime exam material and recur in the practical project, Test 1 and the final.
What this chapter covers
- 01Dissociation constant KD = [A][B]/[AB] = k_off/k_on; at free ligand = KD, 50% of protein is bound; lower KD = higher affinity
- 02IC50: concentration giving 50% inhibition; relative/assay-dependent; optimised drugs reach low nM
- 03Z-factor for assay quality: Z = 1 − 3(σ_pos + σ_neg)/|μ_pos − μ_neg|; bands (1 ideal, 0.5–1 excellent, 0–0.5 marginal, <0 unsuitable)
- 04HTS logistics: robotics, 96/384-well plates, 10⁵–10⁷ libraries, ~$1/molecule, ~10–100 hits at ~1 µM
- 05Lipinski rule of 5: MW < 500, HBD < 5, HBA < 10, cLogP < 5; ≤1 violation for oral (aspirin passes, venetoclax fails)
- 06PAINS (pan-assay interference): redox cyclers, chelators, reactive/fluorescent scaffolds to filter out
- 07Assay readouts: absorbance/colourimetric, fluorescence, fluorescence polarisation, luminescence, AlphaScreen
- 08Targeted versus phenotypic HTS trade-offs
Assay quality from the Z-factor
- +1Write the formula: Z = 1 − 3(σ_pos + σ_neg)/|μ_pos − μ_neg|. It compares the spread of the controls (3 SDs each) with the separation of their means.
- +1Numerator: 3(σ_pos + σ_neg) = 3(0.05 + 0.03) = 3(0.08) = 0.24.
- +1Denominator: |μ_pos − μ_neg| = |1.00 − 0.20| = 0.80. So the ratio is 0.24/0.80 = 0.30, and Z = 1 − 0.30 = 0.70.
- +1Classify: Z = 0.70 falls in the 0.5–1 band = an excellent assay with a wide signal window. Yes — it is suitable for a large HTS campaign; a Z below 0 (control clouds overlapping) would be unusable.
Key terms
- 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; lower KD means tighter binding.
- IC50
- The concentration of an inhibitor that reduces a biological/biochemical activity by 50%. It is relative (assay-dependent); an optimised drug reaches the low-nanomolar range.
- Z-factor
- A dimensionless measure of assay quality, Z = 1 − 3(σ_pos + σ_neg)/|μ_pos − μ_neg|. Z = 1 is ideal, 0.5–1 excellent, 0–0.5 marginal, and Z < 0 means the assay cannot separate signal from noise.
- Lipinski's rule of 5
- MW < 500, HBD < 5, HBA < 10, cLogP < 5; poor oral absorption is likely with more than one violation. A first-pass developability filter on screening hits.
- PAINS
- Pan-assay interference compounds — scaffolds that give false positives across many assays (redox cyclers, chelators, reactive electrophiles, intrinsically fluorescent structures) and should be filtered out.
- High-throughput screening (HTS)
- Automated testing of large libraries (10⁵–10⁷ compounds) in 96/384-well plates at ~$1 per molecule, typically yielding ~10–100 hits around 1 µM that then require optimisation.
High-Throughput Screening & Hit Identification FAQ
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.
What is the difference between KD and IC50?
KD is a thermodynamic binding constant — the concentration at which half the target is occupied — and is a property of the ligand-target pair. IC50 is a functional potency measure — the concentration that halves an activity in a particular assay — and depends on assay conditions such as substrate concentration. KD measures how tightly something binds; IC50 measures how much of it you need to see a 50% effect in that assay.
Why filter screening hits through Lipinski's rule of 5?
Because a hit that binds well but cannot be absorbed orally is a poor starting point. The rule of 5 (MW < 500, HBD < 5, HBA < 10, cLogP < 5, with at most one violation) is a fast flag for oral-absorption problems, so it triages hits toward the ones with a realistic chance of becoming an oral drug. Some modalities (PPI inhibitors, macrocycles) legitimately break it, but for a classic small-molecule hit it is the first check.
Can AI help me with the HTS calculations in MCHM3001?
Yes. Sia can walk you through a Z-factor computation and classification, contrast KD with IC50, count Lipinski violations on a structure, or explain why a PAINS scaffold gives false positives. It explains the method and checks your arithmetic and units; it does not complete graded assessment for you, and University of Sydney academic-integrity rules apply.
Exam move
This is a calculation chapter, so drill the three recurring computations until they are automatic: the Z-factor (keep the factor of 3 on the SDs), a Lipinski violation count against all four limits, and the meaning of KD versus IC50. Practise classifying a Z-factor into its band and stating whether the assay is fit for HTS. Keep a mental library of which readouts suit which target (colourimetric for the MMP-3 practical, fluorescence polarisation, AlphaScreen) and remember to reject PAINS before celebrating a hit. Because these skills reappear in the practical project and hit-to-lead chapter, over-learn them now; when a formula or band slips, ask Sia to set you a fresh worked problem.
Working through High-Throughput Screening & Hit Identification in MCHM3001? Sia is AskSia’s AI Chemistry tutor — ask any MCHM3001 High-Throughput Screening & Hit Identification question and get a clear, step-by-step explanation grounded in how MCHM3001 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
