Evaluate oral bioavailability from rotatable bonds, polar surface area, and molecular descriptors. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Veber's Rule screening
Evaluate oral bioavailability from rotatable bonds, polar surface area, and molecular descriptors. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Veber's Rule screening
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:
| Criterion | Threshold | Rationale |
|---|---|---|
| Rotatable bonds | Molecular flexibility affects membrane permeation and binding entropy | |
| Polar surface area (TPSA) | Ų | Polarity correlates inversely with permeation rate |
An alternative formulation substitutes total hydrogen bond count (donors + acceptors ) for TPSA. Compounds meeting both criteria demonstrate high probability of good oral bioavailability.
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.
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 | Description |
|---|---|
Molecule | SMILES 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. |
| Column | Description |
|---|---|
Name | Compound identifier (auto-generated if not provided) |
SMILES | Input structure |
MW [Da] | Molecular weight |
LogP | Calculated partition coefficient |
HBD | Hydrogen bond donor count (N–H, O–H groups) |
HBA | Hydrogen bond acceptor count (N, O atoms) |
Rot. Bonds | Rotatable bond count |
Violations | Number of Veber criteria exceeded |
Result | Pass if criteria met, Fail otherwise |
Results export to CSV, JSON, or Excel for integration with screening pipelines.
The two rules address different aspects of oral drug-likeness and work best in combination.
| Rule | Focus | Key parameters |
|---|---|---|
| Lipinski (Ro5) | Size and lipophilicity | MW, LogP, HBD, HBA |
| Veber | Flexibility and polarity | Rotatable 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.
| Violations | Interpretation |
|---|---|
| 0 | Favorable flexibility and polarity profile. High probability of good oral absorption. |
| 1 | Borderline. May still achieve acceptable bioavailability depending on other properties. |
| 2 | Both 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.
Veber's rule applies specifically to passive transcellular absorption:
The thresholds derive from rat studies. Human absorption may differ for some compounds, though the general relationships hold across species.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
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:
| Criterion | Threshold | Rationale |
|---|---|---|
| Rotatable bonds | Molecular flexibility affects membrane permeation and binding entropy | |
| Polar surface area (TPSA) | Ų | Polarity correlates inversely with permeation rate |
An alternative formulation substitutes total hydrogen bond count (donors + acceptors ) for TPSA. Compounds meeting both criteria demonstrate high probability of good oral bioavailability.
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.
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 | Description |
|---|---|
Molecule | SMILES 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. |
| Column | Description |
|---|---|
Name | Compound identifier (auto-generated if not provided) |
SMILES | Input structure |
MW [Da] | Molecular weight |
LogP | Calculated partition coefficient |
HBD | Hydrogen bond donor count (N–H, O–H groups) |
HBA | Hydrogen bond acceptor count (N, O atoms) |
Rot. Bonds | Rotatable bond count |
Violations | Number of Veber criteria exceeded |
Result | Pass if criteria met, Fail otherwise |
Results export to CSV, JSON, or Excel for integration with screening pipelines.
The two rules address different aspects of oral drug-likeness and work best in combination.
| Rule | Focus | Key parameters |
|---|---|---|
| Lipinski (Ro5) | Size and lipophilicity | MW, LogP, HBD, HBA |
| Veber | Flexibility and polarity | Rotatable 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.
| Violations | Interpretation |
|---|---|
| 0 | Favorable flexibility and polarity profile. High probability of good oral absorption. |
| 1 | Borderline. May still achieve acceptable bioavailability depending on other properties. |
| 2 | Both 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.
Veber's rule applies specifically to passive transcellular absorption:
The thresholds derive from rat studies. Human absorption may differ for some compounds, though the general relationships hold across species.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.