Module 2: Target Identification, Validation & Druggability
Every other module in this course assumes you already have a target. This one asks the question that comes first — and is the single highest-leverage decision in the entire pipeline: which protein should we drug at all?
1. What Is a Therapeutic Target?
A therapeutic target is a biomolecule — almost always a protein — whose activity can be modulated by a drug to produce a desired clinical effect. To be a genuine target it must satisfy three independent conditions, and failing any one of them is fatal:
1. Disease-linked
Modulating it must actually change the disease. This is a question about biology, and it is where most drugs die.
2. Druggable
It must have a binding site that a drug-like molecule can actually occupy with high affinity. This is a question about structure — and it is what the rest of this module measures.
3. Safely modulable
Hitting it must not cause unacceptable harm — which depends on where else it is expressed and what else the drug touches (Module 12).
2. Why Target Selection Dominates Everything Downstream
The numbers are sobering. The human genome encodes roughly 20,000 proteins, but only about 4,500 are considered druggable at all — and every drug ever approved acts on just 716 distinct targets. The universe of proven targets is startlingly small.
~20,000
protein-coding genes
the whole proteome
~4,500
considered druggable
≈ 22% of the proteome
716
targets of all approved drugs
≈ 3.6% of the proteome
The dominant cause of Phase II failure is lack of efficacy — not toxicity, not pharmacokinetics. A compound that fails for lack of efficacy usually did nothing wrong chemically: it bound its target beautifully. The target was simply the wrong one. No amount of docking, QSAR, or MD later in this course can rescue a badly chosen target. That is why this module comes before the methods.
3. Target Classes
Approved drugs cluster heavily into a handful of protein families, largely because those families happen to have well-formed binding pockets. Knowing the class tells you a great deal about which method to reach for.
| Class | Why it's druggable | Typical approach |
|---|---|---|
| Enzymes (esp. kinases, proteases) | Deep, enclosed catalytic/cofactor pockets evolved to bind small molecules | Structure-based docking; competitive inhibition |
| GPCRs | Orthosteric site buried in the 7-TM helical bundle | Historically ligand-based (few structures); now structure-based |
| Ion channels | Pore and gating sites; but selectivity across the family is hard | Often ligand-based; heavy off-target screening (see hERG, Module 12) |
| Nuclear receptors | Buried hydrophobic ligand-binding domain | Structure-based (see the estradiol pharmacophore, Module 7) |
| Transporters | Substrate sites, but large conformational cycles | Ensemble/state-specific modelling |
| PPIs & transcription factors | Usually not — flat, extended, solvent-exposed interfaces | Fragment-based, stabilizers/glues, or degradation (Module 6) |
4. Target Validation: Building an Evidence Chain
Validation asks: if I modulate this target, does the disease change? No single experiment settles it. Confidence comes from stacking independent, orthogonal lines of evidence — the more independent the sources, the lower the risk.
Human genetic evidence
The strongest signal available. GWAS hits, rare loss-of-function variants, and Mendelian disease genes tie the target to human biology directly — not to a mouse. Targets with supporting human genetic evidence succeed in the clinic roughly twice as often, which is why genetics-led target selection has reshaped the industry.
Genetic perturbation in models
Knockout/knockdown (CRISPR, RNAi) shows whether removing the protein produces the desired phenotype. Caveat: removing a protein is not the same as inhibiting it — a knockout also deletes scaffolding functions a drug would leave intact.
Chemical/pharmacological evidence
A selective tool compound reproduces the phenotype. This is the closest proxy for what a drug will actually do — but only if the tool is genuinely selective. A promiscuous 'tool' validates nothing.
Clinical/translational precedent
Human data: an existing drug on the same pathway, or a biomarker that tracks target engagement in patients.
5. Druggability: Can a Small Molecule Actually Bind?
A validated target is worthless if nothing drug-like can bind it. Druggability is a structural property of the pocket, and — unlike biological validation — it can be computed directly from a 3D structure before a single compound is made.
The first step is finding the pocket at all. Tools fall into three families:
Geometric
Find concave cavities purely from shape, e.g. by rolling a probe sphere over the surface (fpocket, PockDrug). Fast and structure-only.
Energetic / probe-based
Computationally flood the surface with small organic probes and find where they cluster — "hot spots" (FTMap, SiteMap). Captures chemistry, not just shape.
Knowledge-based / ML
Learn what real ligand-binding sites look like from the PDB and predict new ones. Increasingly the default for large-scale proteome-wide assessment.
Once you have a pocket, you score it. The best-known scheme is SiteMap (Halgren, 2009), which reduces a pocket to three numbers and combines them linearly:
Where n is the number of site points (pocket size), e is enclosure (how buried the pocket is, 0–1), and p is hydrophilic character. Read the signs: size and enclosure help, polarity hurts. That single equation explains the entire druggable proteome — and why protein-protein interfaces are so brutally hard.
Interactive Playground: Druggability Scorecard
Load a real pocket type or shape your own, and watch the actual SiteMap equations classify it. Try the key experiment: load the Flat PPI interface, then drag enclosure upward while changing nothing else — and watch an "undruggable" target become druggable. That single slider is the difference between a kinase and a protein-protein interface.
Deep, enclosed, largely hydrophobic — the archetypal druggable pocket.
Capped at 100 by the scoring function.
0 = flat open surface, 1 = fully enclosed cavity.
Higher = more polar/solvent-friendly = worse for binding.
1.20
Dscore
A small molecule with drug-like properties should be able to bind here with high affinity.
≥ 0.80 — this looks like a genuine ligand-binding site, not an incidental surface dent.
Because enclosure carries a +0.60 coefficient and hydrophilicity a −0.324 one, a pocket earns its druggability mainly by being buried and greasy. A PPI interface fails not because it is small — it is usually enormous — but because it is flat and wet. This is exactly why the field invented PROTACs and molecular glues (Modules 1 and 6): if you cannot win the binding-site argument, change the modality so you no longer need a deep pocket.
6. The Inverse Problem: Target Fishing & Polypharmacology
So far we have gone target → molecule. The reverse question is just as important: given a molecule, what does it hit? This is target prediction (or "target fishing"), and it matters for three reasons:
- Phenotypic hits need deconvolution. If a compound cures cells in a screen but you do not know its target, you cannot optimize it rationally.
- Off-targets cause toxicity. Predicting secondary targets early feeds directly into the safety work in Module 12.
- Polypharmacology is sometimes the point. Many effective drugs — notably kinase inhibitors and most CNS drugs — work because they hit several targets. Perfect selectivity is not always the goal.
The dominant computational approach rests on the similar property principle you will meet in Module 5: if your molecule is highly similar to a compound with a known target, it probably shares that target. This turns target prediction into a fingerprint similarity search against annotated bioactivity databases such as ChEMBL.