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Veber's rule

Evaluate oral bioavailability using Veber's criteria for molecular descriptors.

What is Veber's rule?

Veber's rule predicts oral bioavailability based on molecular flexibility and polarity. Published in 2002 by Daniel Veber and colleagues at GlaxoSmithKline, the rule emerged from analysis of over 1,100 drug candidates and their oral absorption in rats.

The rule identifies two key factors independent of molecular weight:

CriterionThresholdRationale
Rotatable bonds10\leq 10Molecular flexibility affects membrane permeation and binding entropy
Polar surface area (TPSA)140\leq 140 ŲPolarity correlates inversely with permeation rate

An alternative formulation substitutes total hydrogen bond count (donors + acceptors 12\leq 12) for TPSA. Compounds meeting both criteria demonstrate high probability of good oral bioavailability.

Scientific basis

Veber's rule addresses molecular flexibility—a factor absent from Lipinski's Rule of 5. Flexible molecules have higher conformational entropy in solution, creating an entropic penalty when adopting the constrained conformations required for membrane permeation. Each rotatable bond adds degrees of freedom that must be restricted during absorption.

Polar surface area provides an alternative measure of hydrogen bonding capacity. TPSA correlates better with permeation rate than LogP because it directly quantifies the polar groups that form hydrogen bonds with water. These bonds must be broken during membrane transit, representing an energetic barrier to absorption.

The relationship between rotatable bonds, polar surface area, and molecular weight explains why the molecular weight cutoff in Lipinski's rule works despite lacking direct mechanistic justification—larger molecules tend to have more rotatable bonds and higher TPSA.

How to use Veber's rule online

ProteinIQ screens compounds against Veber's criteria directly in the browser. Input molecules as SMILES and receive immediate pass/fail classification with calculated descriptors.

Input

InputDescription
MoleculeSMILES strings (one per line). Tab-separated format accepts compound names: aspirin\tCC(=O)Oc1ccccc1C(=O)O. Supports file uploads (.smi, .csv, .txt) and PubChem compound retrieval.

Output

ColumnDescription
NameCompound identifier (auto-generated if not provided)
SMILESInput structure
MW [Da]Molecular weight
LogPCalculated partition coefficient
HBDHydrogen bond donor count (N–H, O–H groups)
HBAHydrogen bond acceptor count (N, O atoms)
Rot. BondsRotatable bond count
ViolationsNumber of Veber criteria exceeded
ResultPass if criteria met, Fail otherwise

Results export to CSV, JSON, or Excel for integration with screening pipelines.

Veber's rule vs. Lipinski's Rule of 5

The two rules address different aspects of oral drug-likeness and work best in combination.

RuleFocusKey parameters
Lipinski (Ro5)Size and lipophilicityMW, LogP, HBD, HBA
VeberFlexibility and polarityRotatable bonds, TPSA/H-bond count

Lipinski's rule predicts whether a compound can passively diffuse across membranes based on physical size and partition behavior. Veber's rule adds conformational considerations—a rigid molecule with high molecular weight may absorb better than a flexible molecule of lower weight.

Approximately 85% of FDA-approved oral drugs conform to Veber's criteria, compared to 66% for Lipinski's rule alone. Compounds passing both rules have the highest likelihood of favorable oral bioavailability.

Interpreting results

ViolationsInterpretation
0Favorable flexibility and polarity profile. High probability of good oral absorption.
1Borderline. May still achieve acceptable bioavailability depending on other properties.
2Both criteria violated. Oral absorption unlikely via passive diffusion.

A compound failing Veber's rule may still reach systemic circulation through active transport mechanisms or specialized formulations. Cyclosporine, for example, violates multiple drug-likeness rules but achieves oral bioavailability through intramolecular hydrogen bonding that masks its polar groups.

Applications

  • Virtual screening: Filtering compound libraries for oral drug candidates before docking or ML-based prediction
  • Lead optimization: Tracking flexibility during structural modifications to maintain drug-like properties
  • Library design: Setting bounds on rotatable bonds and TPSA when enumerating virtual compounds
  • Compound prioritization: Ranking hits by likelihood of acceptable pharmacokinetics

Limitations

Veber's rule applies specifically to passive transcellular absorption:

  • Compounds absorbed via carrier-mediated transport may succeed despite violations
  • The rule does not predict actual bioavailability values, only relative probability
  • Formulation technologies (lipid nanoparticles, solid dispersions) can overcome unfavorable properties
  • Non-oral routes (IV, subcutaneous, inhaled) bypass gastrointestinal absorption entirely

The thresholds derive from rat studies. Human absorption may differ for some compounds, though the general relationships hold across species.

  • Lipinski's Rule of 5: Complementary drug-likeness assessment based on size and lipophilicity
  • Molecular Descriptors: Comprehensive physicochemical property calculation including TPSA
  • Lead-likeness filter: Stricter criteria for fragment and lead optimization
  • ADMET-AI: Machine learning predictions for absorption, distribution, metabolism, excretion, and toxicity
  • Brenk filter: Structural alert screening for problematic functional groups