QEPPI

What is QEPPI?

QEPPI (Quantitative Estimate Index for Compounds Targeting Protein-Protein Interactions) is a drug-likeness scoring function for molecules that target protein-protein interactions. It returns a score from 0 to 1 calibrated against 1,007 experimentally validated PPI modulators from the iPPI-DB database.

Standard drug-likeness rules like Lipinski's Rule of 5 were designed for enzyme inhibitors and reject most successful PPI drugs. PPI binding surfaces are large and flat, requiring bigger and more lipophilic molecules to disrupt them. QEPPI captures this distinct chemical space: its AUC for separating PPI inhibitors from decoys is 0.789, compared to 0.362 for QED on the same set.

How to use QEPPI online

ProteinIQ runs QEPPI calculations in the cloud on any number of compounds at once. Paste SMILES strings into the input box (one per line, optionally tab-separated with names), upload a file, or fetch compounds directly from PubChem by name or CID. Each molecule receives a QEPPI score and the seven underlying molecular descriptors that determine it.

Inputs

Results

Each row in the output corresponds to one input molecule.

Interpreting the score

The default discrimination threshold from the 2021 publication is 0.52, chosen to maximise F-score on the iPPI-DB test set. The continuous score is more useful than a hard cutoff for ranking and prioritization:

The descriptor columns explain why a molecule scored as it did. High MW with low ALogP often drives a low score: PPI inhibitors need both to maintain permeability across a large scaffold. Low TPSA with acceptable HBD/HBA is typical of good PPI compounds. An aromatic ring count of 2–3 tends to be optimal for π-stacking with PPI interfaces.

Molecules scoring near 0.5 sit in borderline chemical space. Combine the score with docking results, synthetic accessibility, and experimental data rather than treating QEPPI alone as a binary accept/reject filter.

When to use QEPPI vs alternatives

Use QEPPI when targeting protein-protein interactions specifically: Bcl-2 family proteins, MDM2/p53, bromodomains, or similar interfaces. Lipinski's Rule of 5 is appropriate for conventional enzyme and receptor targets where oral bioavailability under classical criteria is the priority. ADMET-AI addresses absorption, distribution, metabolism, excretion, and toxicity rather than drug-likeness; it fits later-stage lead optimization after QEPPI has filtered a virtual library. Molecular Descriptors provides the raw physicochemical values without a composite score, useful for building custom QSAR models.

How QEPPI works

QEPPI calculates seven molecular descriptors from SMILES, converts each to a desirability score using an asymmetric double sigmoid function (ADS), then combines them into a final score via weighted geometric mean.

The ADS function fitted to iPPI-DB compounds creates a peak desirability at property values common in successful PPI drugs:

$d(x) = a + \frac{b}{1 + \exp\left(-\frac{x - c + \frac{d}{2}}{e}\right)}\left[1 - \frac{1}{1 + \exp\left(-\frac{x - c - \frac{d}{2}}{f}\right)}\right]$

The six coefficients per descriptor ($a$–$f$) were fitted to the iPPI-DB dataset. The final score is a weighted geometric mean over all seven desirability values:

$\text{QEPPI} = \exp\left(\frac{\sum_i w_i \ln(d_i)}{\sum_i w_i}\right)$

Weights were optimised to maximise discrimination between PPI modulators and non-modulators. The default weight set is the median-optimised configuration from the publication.

The calculation is pure 2D: all descriptors come from SMILES without 3D conformer generation. Stereoisomers receive identical scores. QEPPI also has no structural alert component, so PAINS compounds and reactive groups are not penalised. Run PAINS Filter or Brenk Filter in parallel for those screens.

Macrocycles and large natural product PPI inhibitors (above ~800 Da) tend to score lower than their activity warrants, because they are underrepresented in the iPPI-DB training data. Covalent PPI inhibitors are similarly out-of-distribution; the model has no knowledge of reactive warhead chemistry.

Example: three known compounds

Aspirin scores poorly not because it is a bad drug, but because it occupies chemical space designed for traditional enzyme inhibition. Venetoclax and Navitoclax score well because they were deliberately optimised for the large, hydrophobic Bcl-2 binding groove.

Workflow integration

QEPPI is most useful at the front of the screening funnel, before expensive computations:

1. Virtual screening: Filter large compound libraries (ChEMBL, ZINC) to enrich for PPI-favorable molecules before running docking.
2. Lead optimization: Track QEPPI scores as structural modifications are made. A score drop signals the modification is pushing the compound toward conventional drug space.
3. ADMET assessment: Compounds passing QEPPI should proceed to ADMET-AI for toxicity and pharmacokinetics prediction.
4. Experimental validation: QEPPI identifies molecules worth testing; biochemical or cellular assays provide ground truth.